Due to the health crisis caused by SARS-CoV-2, the creation of a new vaccine platform based on mRNA was implemented. Globally, around 13.32 billion COVID-19 vaccine doses of diverse platforms have been given, and up to this date, 69.7% of the total population received at least one injection of a COVID-19 vaccine. Although these vaccines prevent hospitalization and severe forms of the disease, increasing evidence has shown they do not produce sterilizing immunity, allowing people to suffer frequent re-infections. Recent research has also raised concerns that mRNA vaccines could induce immune tolerance, which, added to that caused by the virus itself, could complicate the clinical course of a COVID-19 infection. Furthermore, recent investigations have found high IgG4 levels in people who were administered two or more injections of mRNA vaccines. It has been suggested that an increase in IgG4 levels could have a protecting role by preventing immune over-activation, similar to that occurring during successful allergen-specific immunotherapy by inhibiting IgE-induced effects. Altogether, evidence suggests that the reported increase in the IgG4 levels detected after repeated vaccination with the mRNA vaccines is not a protective mechanism; rather, it may be a part of the immune tolerance mechanism to the spike protein that could promote unopposed SARS-CoV2 infection and replication by suppressing natural antiviral responses. IgG4-induced suppression of the immune system due to repeated vaccination can also cause autoimmune diseases, promotes cancer growth, and autoimmune myocarditis in susceptible individuals.

Intelligent fault diagnosis techniques have replaced the time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning model can improve the accuracy of intelligent fault diagnosis with the help of its multilayer nonlinear mapping ability. This paper has proposed a novel method named Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN). The proposed method uses raw vibration signals as input (data augmentation is used to generate more inputs), and uses the wide kernels in first convolutional layer for extracting feature and suppressing high frequency noise. Small convolutional kernels in the preceding layers are used for multilayer nonlinear mapping. AdaBN is implemented to improve the domain adaptation ability of the model. The proposed model addresses the problem that currently, the accuracy of CNN applied to fault diagnosis is not very high. WDCNN can not only achieve 100% classification accuracy on normal signals, but also outperform state of the art DNN model which is based on frequency features under different working load and noisy environment.

This study sought to investigate steel frames’ performance with dual lateral loader system (Frame bending + bracings of divergent and convergent) in near and far filed to fault. In order to this, four categories of steel frame with dual system with 8, 10 and 12 story are designed with average formation based on existing seismic regulation in 2800 standard of Iran and tenth chapter of national regulations of construction (planning and performing steel construction). Time history non-linear dynamic analysis under the effect of near and far field earthquakes has been done on plan’s models. Then the maximum of floors’ dislocation, floors’ drift, roof dislocation, base shear and energy curves of frames are shown and compared with each other. All non-linear time history analyses have been accomplished using PERFORM 3D software.

Ectopic expression of the MYB transcription factor of AmROSEA1 from Antirrhinum majus has been reported to change anthocyanin and other metabolites in several species. In this study, we found that overexpression of AmRosea1 significantly improved the tolerance of transgenic rice to drought and salinity stress. Transcriptome analysis revealed that a considerable amount of stress-related genes were affected by exogenous AmRosea1 during both drought and salinity stress treatments. These affected genes are involved in stress signal transduction, the hormone signal pathway, ion homeostasis and the enzymes that remove peroxides. This work suggests that the AmRosea1 gene is a potential candidate for genetic engineering of crops.

Properties of time domain parameters of the vibration signal have been extensively studied for the fault diagnosis of rolling element bearings (REB). Parameters like kurtosis and Envelope Harmonic-to-Noise Ratio are most widely applied in this field and some important progress has been made. However, since only one-sided information is contained in these parameters respectively, problems still exist in practice when the signals collected are of complicated structure and/or contaminated by strong background noises. A new parameter, named Shock pulse index (SPI), is proposed in this paper. It integrates the mutual advantage of both parameters above and can help effectively identify fault related impulse components under the interference of strong background noises, unrelated harmonic components and random impulses. The SPI optimizes the parameters of Maximum Correlated Kurtosis Deconvolution (MCKD), which is used to filter the signals under consideration. Finally, the interested transient information contained in the filtered signal can be highlighted through demodulation with Teager Energy Operator (TEO). Fault related impulse components can therefore be extracted accurately. Simulations and experiment analyses verify the effectiveness and correctness of the SPI.

The critical hypoxia in COVID-19 patients during this pandemic, has taken away many lives all around the globe. The mechanism has been poorly understood and initially, word got around that it was a SARS (Severe Acute Respiratory Syndrome) pneumonia. The atypical images in lung computerized axial tomography (CAT) scans were alarming. This immediately alerted everyone including poor countries to purchase lacking sophisticated ventilator equipment. However, in some countries, even 88% of the patients on ventilators lost their lives. New observations and pathological findings are gradually clarifying the disease. What seems evident is that it is not only one disease but several, with different responses in different countries and different altitudes. The critical hypoxia and «gasping» present in some patients are not totally understood. It was mentioned that it could be like a High-Altitude Pulmonary Edema (HAPE). Hereby, as high-altitude medicine and hypoxia physiology specialists, we compare the pathophysiology with that of high-altitude exposure in order to understand the mechanisms involved. Some differences in lung radiological images along with transmission and viral attack mechanisms are discussed. The oxygen transport triad used at high-altitude can be applied on this pathology in order to propose even the use of erythropoietin (EPO) early in the treatment. The immune system is the most important long-term survival tool, so we suggest a short-term strategy: the use of special Earth open-circuit astronaut-resembling suits with effective outside air filtering re-breathing mechanisms in order to return to work and daily activities, without contamination risk. Thereby, the curve can be flattened without quarantine and the economy could recover.

Knowledge-based approaches are useful alternatives to predict the Failure Probability (FP) coping with the insufficient data, process integrity, and complexity issue in manufacturing systems. This study proposes a Fault Tree Analysis (FTA) approach as proactive knowledge-based technique to estimate the FP based maintenance planning with subjective information from domain experts. However, the classical-FTA still suffers from uncertainty and static structure limitations which poses a substantial dilemma in predicting FP. To deal with the uncertainty issues, a Fuzzy-FTA (FFTA) model was developed by statistical analysing the effective attributes such as experts' trait impacts, scales variation and, assorted membership and defuzzification functions. Besides, a Bayesian Network (BN) theory was conducted to overcome the static limitation of classical-FTA. The results of FFTA model revealed that the changes in decision attributes were not statistically significant on FP variation while BN model considering conditional rules to reflect the dynamic relationship between events had more impact on predicting FP. After all, the integrated FFTA-BN model was used in the optimization model to find the optimal maintenance intervals according to estimated FP and the total expected cost. As a practical example, the proposed model was implemented in a semi-automatic filling system in an automotive production line. The outcomes could be useful for upgrading the availability and safety of complex equipment in manufacturing systems.

Salinity is a known factor which shapes population dynamics and community structure through direct and indirect effects towards aquatic ecosystems. The responses of Moina macrocopa (Cladocera) collected from Setiu Wetland lagoon (Terengganu) were evaluated through manipulative laboratory experiments to understand the ability of M. macrocopa to tolerate under high salinity stress. Specifically, the fatty acid composition, growth, survival and reproduction of this cladocerans species were examined. Sodium chloride (NaCl) were used in the treatments water with the concentration 0, 4, 6, 8, 12, and 15psu. Fatty acid levels were determined using Gas Chromatography and Mass Spectrophotometry (GC-MS). Results clearly indicate that normal conditions give the highest content of fatty acid, especially the polyunsaturated fatty acid content like EPA (eicosapentaenoic acid), ALA (alpha-linoleic acid), ARA (arachidonic acid) and DHA (docosahexaenoic acid). Furthermore, M. macrocopa survival also was best at 0psu, with percentage reached 98%, whereas the opposite occurred at 15psu, with approximately 9% of viable animals survived. On other aspects, M. macrocopa also showed the highest reproduction rate at 0psu (e.g. average initial age of reproduction, 4.33±0.58 days) compared with other salinities level. Interestingly, the difference in terms of growth at different salinities was unapparent, an unexpected outcome when adverse effects such as osmoregulation pressure on the organism are considered. Based on the results, we conclude that M. macrocopa can only tolerate salinity below 8psu and, is unable to withstand stressful environmental condition engendered with salinities above 8psu.

The fault diagnosis of wind farms has been proven to be a challenging task and motivates the research activities carried out through this work. Therefore, this paper deals with the fault diagnosis of a wind park benchmark model, and it considers viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator involves data-driven approaches, as they can represent effective tools for coping with poor analytical knowledge of the system dynamics, noise, uncertainty and disturbances. In particular, the proposed data-driven solutions rely on fuzzy models and neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen architectures rely on nonlinear autoregressive with exogenous input models, as they can represent the dynamic evolution of the system along time. The developed fault diagnosis schemes are tested by means of a high-fidelity benchmark model, that simulates the normal and the faulty behaviour of a wind farm installation. The achieved performances are also compared with those of a model-based approach relying on nonlinear differential geometry tools. Finally, a Monte-Carlo analysis validates the robustness and the reliability of the proposed solutions against typical parameter uncertainties and disturbances.

Background/Goals: The gut hormone PYY secreted from intestinal L-cells has been implicated in the mechanisms of satiation via Y2-receptor (Y2R) signaling in the brain and periphery and is a major candidate for mediating the beneficial effects of bariatric surgery on appetite and body weight. Methods: Here we assessed the role of Y2R signaling in the response to low- and high-fat diets and its role in the effects of Roux-en-Y gastric bypass (RYGB) surgery on body weight, body composition, food intake, energy expenditure and glucose handling, in global Y2R-deficient (Y2RKO) and wildtype mice made obese on high-fat diet. Results: Both male and female Y2RKO mice responded normally to low- and high-fat diet in terms of body weight, body composition, fasting levels of glucose and insulin, as well as glucose and insulin tolerance for up to 30 weeks of age. Contrary to expectations, obese Y2RKO mice also responded similarly to RYGB compared to WT mice for up to 20 weeks after surgery, with initial hypophagia, sustained body weight loss, and significant improvements in fasting insulin, glucose tolerance, HOMA-IR, and liver weight compared to sham-operated mice. Furthermore, non-surgical Y2RKO mice weight-matched to RYGB showed the same improvements in glycemic control as Y2RKO mice with RYGB that were similar to WT mice. Conclusions: PYY signaling through Y2R is not required for the normal appetite-suppressing and body weight-lowering effects of RYGB in this global knockout mouse model. Potential compensatory adaptations of PYY signaling through other receptor subtypes or other gut satiety hormones such as GLP-1 remain to be investigated.

Contrary to the long-held belief that the effects of vaccines are specific for the disease they were created; compelling evidence has demonstrated that vaccines can exert positive or deleterious non-specific effects (NSEs). In this review, we compiled research reports from the last 40 years, showing that live vaccines induce positive NSEs, whereas non-live vaccines induce several negative NSEs, including increased female mortality associated with enhanced susceptibility to other infectious diseases, especially in developing countries. These negative NSEs are determined by the vaccination sequence, the antigen concentration in vaccines, the type of vaccine used (live vs. non-live), and also by repeated vaccination. We do not recommend stopping using non-live vaccines, as they have demonstrated to protect against their target disease, so the suggestion is that their detrimental NSEs can be minimized simply by changing the current vaccination sequence. High IgG4 antibody levels generated in response to repeated inoculation with mRNA COVID-19 vaccines could be associated with a higher mortality rate from unrelated diseases and infections by suppressing the immune system. Since most COVID-19 vaccinated countries are reporting high percentages of excess mortality not directly attributable to deaths from such disease, the NSEs of mRNA vaccines on overall mortality should be studied in depth.

The ectoparasitic mite Varroa destructor is the most significant pathological threat to the western honey bee, Apis mellifera, leading to the death of most colonies if left untreated. An alternative approach to chemical treatments is to selectively enhance heritable honey bee traits of resistance or tolerance to the mite through breeding programs, or select for naturally surviving untreated colonies. We conducted a literature review of all studies documenting traits of A. mellifera populations either selectively bred or naturally selected for resistance and tolerance to mite parasitism. This allowed us to conduct an analysis of the diversity, distribution and importance of the traits in different honey bee populations that can survive V. destructor throughout the globe. In a second analysis, we investigated the genetic bases of these different phenotypes by comparing ’omics studies (genomics, transcriptomics, and proteomics) of A. mellifera resistance and tolerance to the parasite. Altogether, this review provides a detailed overview of the current state of the research projects and breeding efforts against the most devastating parasite of A. mellifera. By highlighting the most promising traits of varroa-surviving bees and our current knowledge on their genetic bases, this work will help direct future research efforts and selection programs to control this pest. Additionally, by comparing the diverse populations of honey bees that exhibit the traits, this review highlights the consequences of anthropogenic and natural selection on the interactions between hosts and parasites.

The burdens of malnutrition, protein and micronutrient deficiency, and obesity cause enormous costs to society. Crop nutritional quality has been compromised by the emphasis on edible yield and through the loss of biodiversity due to the introduction of high-yielding uniform cultivars. Heirloom crop cultivars are traditional cultivars grown for a long time (> 50 years), and whose heritage has been preserved by regional, ethnic or family groups. Heirlooms are recognized for their unique appearance, names, uses and historical significance. They are gaining in popularity because of their unique flavors and cultural significance to local cuisine, and their role in sustainable food production for small-scale farmers. As a contrast to modern cultivars, heirlooms may offer a welcome alternative in certain markets. Recently, market channels have emerged for heirloom cultivars in the form of farmer-breeder-chef collaborations and seed savers organizations. There is therefore urgent need to know more about the traits available in heirloom cultivars, particularly for productivity, stress tolerance, proximate composition, sensory quality and flavor. This information is scattered and the intention of this review is to document some of the unique characteristics of heirloom cultivars that may be channeled into breeding programs for developing locally adapted high value cultivars.

In this work, a hybrid component Fault Detection and Diagnosis (FDD) approach for industrial sensor systems is established and analyzed, to provide a hybrid schema that combines the advantages and eliminates the drawbacks of both model-based and data-driven methods of diagnosis. Moreover, it shines the light on a new utilization of Random Forest (RF) together with model-based diagnosis, beyond its ordinary data-driven application. RF is trained and hyperparameter tuned using three-fold cross validation over a random grid of parameters using random search, to finally generate diagnostic graphs as the dynamic, data-driven part of this system. This is followed by translating those graphs into model-based rules in the form of if-else statements, SQL queries or semantic queries such as SPARQL, in order to feed the dynamic rules into a structured model essential for further diagnosis. The RF hyperparameters are consistently updated online using the newly generated sensor data to maintain the dynamicity and accuracy of the generated graphs and rules thereafter. The architecture of the proposed method is demonstrated in a comprehensive manner, and the dynamic rules extraction phase is applied using a case study on condition monitoring of a hydraulic test rig using time-series multivariate sensor readings.

This paper investigates how Islamic tolerance contributes to solve religious conflict today that has impacted on various aspects such as economic collapse, insecurity in life, lack of professional society, academic retrogressions, and so on. Knowledge of these issues has become an urgent to build a community that is full of peace, harmony and unity. The paper suggests that Quranic views on tolerance should be understood and practiced in order to avoid disunity in society which is due to failure to appreciate tolerance values in association. The paper concludes problems or misunderstandings always occur in different religious societies, since every religion has its own teachings and principles, as well as each of them is interpreted in different ways.

Farmers and seed companies constantly require high-quality seeds with excellent agronomic performance. However, faced with environmental adversity, limited natural resources and increasing food demand around the globe, more attention has turned to improving crop plant production by implementing efficient strategies. Seed priming technology has shown promising biological improvements leading to suitable agronomic performance in crop plants under adverse environmental conditions. Seeds are subjected to controlled conditions that are conducive to complex physiological, biochemical, and molecular changes, conferring specific stress tolerance to subsequent germination and growth conditions. In this review paper, we aimed to study the recent approaches in the efficiency of hydropriming, osmopriming, chemopriming, hormopriming, nanopriming, matrix priming, biopriming, physical priming and hybrid priming procedures in the production of crop plants under environmental adversity, as well as their biological mechanism changes. All priming methods demonstrated relevant changes in the biological mechanism related to crop plant production by mitigating salinity effects, heavy metals, and flooding stress and enhancing chilling, heat, drought and phytopathogen tolerance. We strongly recommend that researchers combine multiple priming methods, known as hybrid priming, in their investigations to provide novel technologies and additional biological approaches to enhance the knowledge of crop plant science. Thus, the findings shed light on the use of seed priming technology as a key strategy to increase crop plant production under environmental adversity by acquiring stress tolerance and enhancing agronomic traits to meet the global food demand.

This manuscript provides a new integrated view of the development of allergen-specific TH2 and the lack of their associated Treg cells in a unified model, justifying the need for IL-2 to correct allergy conditions.

Doubly Fed Induction Generator (DFIG) has a stator winding directly coupled with grid. Whereas, rotor winding is connected via a fault-prone back to back power converters. DFIG is known to be vulnerable to the grid faults. In early times, when a fault occurred, these generators were required to disconnect from the grid to secure the generator and power converters. However, due to the increased penetration of wind turbines into the power system, grid operators demanded that the wind turbines remain connected to the grid, as disconnecting them would further disrupt the grid. When a fault at the grid terminal occur, a high stator current is induced which further result in high rotor current. This current will trigger the DC-link voltage to rise. This high currents and DC-link voltage will cause harm to the converters. Thus, in this paper work, the crowbar protection system is employed for protecting the converters against excess energy. Furthermore, the analysis of DFIG is rendered by integrating the crowbar protection with the Battery Energy Storage System (BESS) for a much effective outcome in enhancing machine to drive the fault. MATLAB-Simulink software is used for modeling and simulation. All system parameters are obtained from ADAMA-II Wind Farm.

The paper presents dynamic and transient behaviour of the Doubly Fed Induction Generator (DFIG) in the wind farms. When Voltage sag or any faults occurs in the network, the variables in the doubly Fed Induction Generators are varying severely. If the voltage sag occurs, Active and Reactive power that generated by the DFIG will decrease and will increase respectively, The DC voltage link will be bigger, and the rotor current will increase. The DFIG in the wind farms uses Proportional Integral controller for controlling of electronic devices (Rotor Side Converter and Grid Side Converter). Even though the model of Doubly Fed Induction Generators and electronic device in the paper are linear, The Proportional Integral controllers cannot protect and control the variables. So, the power electronic converters and the DC-link can damage due to over voltages and over currents. Doubly Fed Induction Generator in the wind farm is simulated in MATLAB software. The results of the simulation present over voltage and over current in the DC-link and in the rotor of the Doubly Fed Induction Generator. In addition, it will explore the effect of Proportional Integral controller in Doubly Fed Induction Generator when three-phase short circuit fault occurs

Preliminaries Convolutional Neural Network (CNN) applications have recently emerged in Structural Health Monitoring (SHM) systems focusing mostly on vibration analysis. However, the SHM literature shows clearly that there is a lack of application regarding the combination of PZT (Lead Zirconate Titanate) based method and CNN. Likewise, applications using CNN along with the Electromechanical Impedance (EMI) technique applied to SHM systems are rare. To encourage this combination, an innovative SHM solution through the combination of the EMI-PZT and CNN is presented here. To accomplish this, the EMI signature is split into several parts followed by computing the Euclidean distances among them to form a RGB (red, green and blue) frame. As a result, we introduce a dataset formed from the EMI-PZT signals of 720 frames, encompassing a total of 4 types of structural conditions for each PZT. In a case study, the CNN-based method was experimentally evaluated using three PZTs glued onto an aluminum plate. The results reveal an effective pattern classification; yielding a 100% hit rate which outperforms other SHM approaches. Furthermore, the method needs only a small dataset for training the CNN, providing several advantages for industrial applications.

Load balancing, energy efficiency and fault tolerance are among the most important data dissemination issues in Wireless Sensor Networks (WSNs). In order to successfully cope with the mentioned issues, two main approaches (namely, Data-centric Storage and Distributed Data Storage) have been proposed in the literature. Both approaches suffer from data loss due to memory and/or energy depletion in the storage nodes. Even though several techniques have been proposed so far to overcome the mentioned problems, the proposed solutions typically focus on one issue at a time. In this paper, we integrate the Data-centric Storage (DCS) features into Distributed Data Storage (DDS) mechanisms and present a novel approach, denoted as Collaborative Memory and Energy Management (CoMEM), to overcome both problems and bring memory and energy efficiency to the data loss mechanism of WSNs. We also propose analytical and simulation frameworks for performance evaluation. Our results show that the proposed method outperforms existing approaches in various WSN scenarios.

Primary importance is devoted to Fault Detection and Diagnosis (FDI) of electrical machine and drive systems in modern industrial automation. The widespread use of Machine Learning techniques has made it possible to replace traditional motor fault detection techniques with more efficient solutions that are capable of early fault recognition by using large amounts of sensory data. However, the detection of concurrent failures is still a challenge in the presence of disturbing noises or when the multiple faults cause overlapping features. The contribution of this work is to propose a novel methodology using multi-label classification method for simultaneously diagnosing multiple faults and evaluating the fault severity under noisy conditions. Performance of various multi-label classification models are compared. Current and vibration signals are acquired under normal and fault conditions. The applicability of the proposed method is experimentally validated under diverse fault conditions such as unbalance and misalignment.

In this research work, an adaptive scheme for the coordinated protection of AC Microgrids using directional overcurrent (DOCR) relays is presented. Protection of AC MGs is a complex and challenging issue due to the dynamic nature of the network including, a) its capability to reconfigure the modes of operation ranging from grid-connected to the islanded mode, c) bidirectional-power flow capability, and c) integration of intermittent renewable energy resources with real-time variations in the resource availability. Consequently, the fault current contributions may largely vary depending upon the incident conditions on the network. Conventional protection schemes, generally designed for radial networks, and unidirectional power flow from the source end to the load may either mal-operate or exhibit very poor performance, if not adapted according to the dynamic conditions of the network. To address this issue, a communication-based adaptive protection scheme capable to adapt its settings according to the generation resource availability and network configuration is presented in this work. The proposed scheme consists of an intelligent central protection unit (ICPU) capable to update the settings and communicate it to the individual relays based on the pre-calculated offline settings. The directional overcurrent relays employed in the scheme use two-stage settings, i.e. definite time and inverse definite minimum time characteristics for the effective coordination among the downstream and upstream relays. An adaptive algorithm for ICPU operation is presented and a case study is implemented for a modified IEEE 9-bus system using DigSilent Power factory. The results for various scenarios including, a) grid-connected mode of operation, b) islanded mode of operation, and c) variable distributed generation mode are obtained and compared to the static scheme, which validates the effectiveness of the proposed scheme.

In this study, we evaluated the potential of fungal endophytes to control yellow rust in wheat (Triticum aestivum L.) as endophytes are beneficial microbes and alternate to pesticides for confronting pathogens. The in-vitro efficacy of the fungal endophytes isolated from different desert plants was evaluated and the best four namely Colletotrichum lindemuthianum, Piriformospora indica, Acremonium lolii and Trichoderma viride were selected. Seeds of two susceptible wheat genotypes namely Fareed-06 and Shafaq-06 obtained from screening experiment were inoculated by dipping in four endophytic spore suspensions and were sown using randomized complete block design under factorial arrangement. Data concerning about area under disease progress curve, final disease severity percentage, coefficient of infection,1000- grains weight and grain yield were recorded. Results showed that endophyte P. indica showed significant decrease in final disease severity (FDS) and area under disease progress curve (AUDPC), resultantly 12.2% grain yield gain in rust susceptible wheat genotypes of Fareed-06 and Shafaq-06 followed by the endophytes T. viride, C. lindemuthianum and A. lolii with the grain yield gain of 10.6%, 06.2% and 04.2% respectively. In crux, fungal endophytes are valuable microbes which can be employed to induce tolerance against P. striiformis in yellow rust vulnerable areas for better and sustainable wheat production.

Clinical agreement between two quantitative measurements on a group of subjects is generally assessed with the help of the Bland-Altman (B-A) limits. The interpretation regarding agreement is based on whether B-A limits are within the pre-specified clinical tolerance. Thus, clinical tolerance limits are necessary for this method. We argue in this communication that such limits of clinical tolerance can be directly used for assessing agreement and plead that this nonparametric approach is simple and robust to the distribution pattern and outliers. Such direct use of clinical tolerance limits has more flexibility, and it is more effective in assessing the extent of agreement.

Heat stress occurring during the reproductive stage of wheat has a detrimental effect on productivity. A durum wheat core set was exposed to simulated terminal heat stress by applying plastic tunnels at the time of flowering over two seasons. Mean grain yield was reduced by 54% compared to control conditions, and grain number was the most critical trait for tolerance to this stress. The combined use of tolerance indices and grain yield identified five elites: Kunmiki, Berghouata1, Margherita2, IDON37-141, and Ourgh. The core set was also subjected to genome wide association study using 7,652 polymorphic SNPs markers. The most critical genomic regions were identified in association with spike fertility and tolerance indices on chromosome 1A, 5B and 6B. Haplotype analysis on a set of 208 elites confirmed that lines that carried the positive allele at all three QTLs resulted in a yield advantage of 8% when field tested under daily temperatures above 31° C. Two of the QTLs were successfully validated into KASP markers and explained >10% of the phenotypic variation for an independent elite germplasm set. These genomic regions can now be readily deployed via breeding to improve resilience to climate change and increase productivity in heat-stressed areas.

SARS-CoV-2, the virus that causes theCOVID-19 disease, has been demonstrated to cause immune suppression in certain individuals. This can manifest as a reduced ability for the host's immune system to effectively control the infection. Studies have reported that patients with COVID-19 can exhibit a decline in white blood cell counts, including natural killer cells and T cells, which are integral components of the immune system's response to viral pathogens. These cells play critical roles in the immune response to viral infections, and their depletion can make it harder for the body to mount an effective defense against the virus. Additionally, the virus can also directly infect immune cells, further compromising their ability to function. Some individuals with severe COVID-19 pneumonia may develop a "cytokine storm," an overactive immune response that may result in tissue damage and organ malfunction. The underlying mechanisms of immune suppression in SARS-CoV-2 are not entirely comprehended at this time, and ongoing research is being conducted to gain a more comprehensive understanding. Research has shown that severe SARS-CoV-2 infection promotes the synthesis of IgG4 antibodies. In this work, we propose the hypothesis that the IgG4 antibody produced by B cells in response to infection by SARS-CoV2 generates immunological tolerance that prevents its elimination, and leads to persistence and chronic infection. In sum, we believe that this constitutes another immune evasion mechanism that bears striking similarities to that developed by cancer cells to evade immune surveillance.

This paper presents a novel adaptive fault-tolerant neural-based control design for wind turbines with unknown dynamic and unknown wind speed. By utilizing the barrier Lyapunov function in the analysis of the Lyapunov direct method, the constrained behavior of the system is provided in which the rotor speed, its variation and generated power remain in the desired bounds. In addition, input saturation is also considered in terms of smooth pitch actuator bounding. Furthermore, by utilizing a Nussbaum-type function in designing the control algorithm, the unpredictable wind speed variation is captured without requiring accurate wind speed measurement, observation or estimation. Moreover, with the proposed adaptive analytic algorithms, together with the use of radial basis function neural networks, a robust adaptive and fault-tolerant control scheme is developed without the need for precise information about the wind turbine model nor the pitch actuator faults. Additionally, the computational cost of the resultant control law is reduced by utilizing a dynamic surface control technique. The effectiveness of the developed design is verified using theoretical analysis tools and illustrated by numerical simulations on a high-fidelity wind turbine benchmark model with different fault scenarios. Comparison of the achieved results to the ones that can be obtained via an available industrial controller shows the advantages of the proposed scheme.

Current theories of autoimmunity are diverse, sometimes contradictory, and suffer from incompleteness. Although substantial evidence exists that adaptive and innate immunity, sex, genetic predisposition, and the microbiome all play essential roles in autoimmune disease etiologies and pathogenesis, and that antigen processing is altered during disease induction, no existing theory integrates all of these factors through a single, coherent mechanism. In an attempt to focus the field on the need to elucidate such an integrative mechanism, I propose one possibility here that, if nothing else, helps to identify the nature of the problems that need to be addressed. My theory is that autoimmune diseases are induced by normal immunological responses to unique pairs of complementary antigens, at least one of which is a molecular mimic of a host target. Each antigen in the complementary pair induces a complementary immune response (T or B cell); although each immune response is idiotypic in origin, the antigenic complementarity results in what appears to be an idiotype-anti-idiotype relationship between the responses. Additionally, because of the antigenic complementarity, each immune response mimics one of antigens, abrogating the distinction between self and non-self. If at least one of the antigens mimics a host antigen, then the resulting immunological civil war spreads to a host tissue. Complementary antigens also alter antigen processing so that antigens that would normally be proteolytically digested are presented by the major histocompatibility complex (MHC) to T and B cell receptors inducing a cross-reactive immune response. The resulting civil war is supported by the innate immune system due to the complementarity of the initiating antigens.. Complementary antigens stimulate synergistic toll-like receptors (TLR) and/or nucleotide-binding oligomerization receptors (NOD) resulting in up-regulation of cytokine production and further stimulation of the adaptive immune response. Because the immune responses (e.g., antibodies) mimic the initiating antigens, this synergistic activation of innate immunity becomes chronic. Additionally, TLR and NOD function are highly sensitive to sex hormones, some becoming up-regulated and some down-regulated in the presence of either testosterone or estrogens. This sensitivity explains how sex modifies susceptibility to autoimmune diseases. Genetic mutations in TLR, NOD and MHC further alter antigen presentation and the degree to which antigens stimulate an immune response explaining how genetics also modifies susceptibility. Finally, sex hormones also alter the host microbiome, which in turn modulates autoimmune disease risk by shaping the immunological nature of self and by mediating susceptibility to microbial infection. Moreover, it appears that the microbiome camouflages itself from the immune system by mimicking the host antigenic repertoire; the mimicry between the antigens of the microbiome and the host results in selective attacks on microbiome constituents concomitant with any autoimmune attack on host tissues. This antigenic complementarity theory thereby integrates all major elements known to affect, or be affected by, autoimmune diseases and provides a set of testable implications.

The vibration signal of heavy gearbox presents non-stationary and nonlinear characteristics, which increases the difficulty to extract the fault feature. When the gear has a subtle fault, it may cause a perceptible change of local fluctuation rather than the large scale fluctuation. Therefore, the feature parameters extracted from local fluctuation can effectively improve the recognition performance of the gear fault. In this paper, a novel signal processing method based on variational mode decomposition (VMD) and detrended fluctuation analysis (DFA) is proposed to identify the gear fault of heavy gearbox. Firstly, the raw vibration signal is decomposed several mode components by VMD, which is an adaptive and non-recursive signal decomposition method. Next, the sensitive mode component is selected by a maximal indicator, which is composed of kurtosis and correlation coefficient of relative higher frequency mode components corresponding to local fluctuation of raw vibration signal. Finally, the characteristics of the double-scales feature parameters of selected sensitive mode are extracted by DFA. In addition, the position of turning point of double scales is estimated by sliding windowing algorithm. The proposed method is evaluated through its application to gear fault classification using vibration signal. The results demonstrates that the recognization rate of gear faults condition have marked improvement by proposed method than the DFA of Small Time Scale (STS-DFA) method.

This research was conducted on the Maribaya Timur school community in Lembang Subdistrict, West Bandung Regency, Indonesia, which is an active community in the area that is threatened by the potential for earthquake disasters due to the active Lembang fault. Disaster risk reduction efforts are pursued through increasing school-based preparedness that involves members of the school community, surrounding communities and various institutions that are associated with reducing the risk of school-based earthquake. Increasing preparedness against earthquakes focuses more on aspects of capacity building of school communities in reducing disaster risk, while aspects of vulnerability and threats have not been the focus of disaster risk reduction. The steps taken refer to the element of preparedness by aligning with the conditions, needs and potential that exist in the school community. Theoretically, if the school community has preparedness to face an earthquake disaster, the risk of earthquake disaster in the school community will be reduced so that it can minimize losses, victims and suffering that will be caused by the earthquake disaster.

A new requirement for the manufacturing companies in Industry 4.0 is to be flexible with respect to changes in demands, requiring them to react rapidly and efficiently on the production capacities. Together with the trend to use Service-Oriented Architectures (SOA), this requirement induces a need for agile collaboration among supply chain partners, but also between different divisions or branches of the same company. In order to address this collaboration challenge, we~propose a novel pragmatic approach for the process analysis, implementation and execution. This~is achieved through sets of semantic annotations of business process models encoded into BPMN 2.0 extensions. Building blocks for such manufacturing processes are the individual available services, which are also semantically annotated according to the Everything-as-a-Service (XaaS) principles and stored into a common marketplace. The optimization of such manufacturing processes combines pattern-based semantic composition of services with their non-functional aspects. This is achieved by means of Quality-of-Service (QoS)-based Constraint Optimization Problem (COP) solving, resulting in an automatic implementation of service-based manufacturing processes. The produced solution is mapped back to the BPMN 2.0 standard formalism by means of the introduced extension elements, fully detailing the enactable optimal process service plan produced. This approach allows enacting a process instance, using just-in-time service leasing, allocation of resources and dynamic replanning in the case of failures. This proposition provides the best compromise between external visibility, control and flexibility. In this way, it provides an optimal approach for business process models' implementation, with a full service-oriented taste, by implementing user-defined QoS metrics, just-in-time execution and basic dynamic repairing capabilities. This paper presents the described approach and the technical architecture and depicts one initial industrial application in the manufacturing domain of aluminum forging for bicycle hull body forming, where the advantages stemming from the main capabilities of this approach are sketched.

The aim of this paper is to introduce a new methodology for the fault diagnosis of induction machines working in transient regime, when time-frequency analysis tools are used. The proposed method relies on the use of the optimized Slepian window for performing the short time Fourier transform (STFT) of the stator current signal. It is shown that for a given sequence length of finite duration the Slepian window has the maximum concentration of energy, greater than can be reached with a gated Gaussian window, which is usually used as analysis window. In this paper the use and optimization of the Slepian window for fault diagnosis of induction machines is theoretically introduced and experimentally validated through the test of a 3.15 MW induction motor with broken bars during the start-up transient. The theoretical analysis and the experimental results show that the use of the Slepian window can highlight the fault components in the current's spectrogram with a significant reduction of the required computational resources.

This paper describes a novel thruster fault-tolerant control system (FTC) for open-frame remotely operated vehicles (ROVs). The proposed FTC consists of two subsystems: a model-free thruster fault detection and isolation subsystem (FDI) and a fault accommodation subsystem (FA). The FDI subsystem employs fault detection units (FDUs), associated with each thruster, to monitor their state. The robust, reliable and adaptive FDUs use a model-free pattern recognition neural network (PRNN) to detect internal and external faulty states of the thrusters in real time. The FA subsystem combines information provided by the FDI subsystem with predefined, user-configurable actions to accommodate partial and total faults and to perform an appropriate control reallocation. Software-level actions include penalisation of faulty thrusters in solution of control allocation problem and reallocation of control energy among the operable thrusters. Hardware-level actions include power isolation of faulty thrusters (total faults only) such that the entire ROV power system is not compromised. The proposed FTC system is implemented as a LabVIEW virtual instrument (VI) and evaluated in virtual (simulated) and real-world environments. The proposed FTC module can be used for open frame ROVs with up to 12 thrusters: eight horizontal thrusters configured in two horizontal layers of four thrusters each, and four vertical thrusters configured in one vertical layer. Results from both environments show that the ROV control system, enhanced with the FDI and FA subsystems, is capable of maintaining full 6 DOF control of ROV in the presence of up to 6 simultaneous total faults in the thrusters. With the FDI and FA subsystems in place the control energy distribution of the healthy thrusters is optimised so that the ROV can still operate in difficult conditions under fault scenarios.

Software fault prediction is the very consequent research topic for software quality assurance. Data driven approaches provide robust mechanisms to deal with software fault prediction. However, the prediction performance of the model highly depends on the quality of dataset. Many software datasets suffers from the problem of class imbalance. In this regard, under-sampling is a popular data pre-processing method in dealing with class imbalance problem, Easy Ensemble (EE) present a robust approach to achieve a high classification rate and address the biasness towards majority class samples. However, imbalance class is not the only issue that harms performance of classifiers. Some noisy examples and irrelevant features may additionally reduce the rate of predictive accuracy of the classifier. In this paper, we proposed two-stage data pre-processing which incorporates feature selection and a new Rough set Easy Ensemble scheme. In feature selection stage, we eliminate the irrelevant features by feature ranking algorithm. In the second stage of a new Rough set Easy Ensemble by incorporating Rough K nearest neighbor rule filter (RK) afore executing Easy Ensemble (EE), named RKEE for short. RK can remove noisy examples from both minority and majority class. Experimental evaluation on real-world software projects, such as NASA and Eclipse dataset, is performed in order to demonstrate the effectiveness of our proposed approach. Furthermore, this paper comprehensively investigates the influencing factor in our approach. Such as, the impact of Rough set theory on noise-filter, the relationship between model performance and imbalance ratio etc. comprehensive experiments indicate that the proposed approach shows outstanding performance with significance in terms of area-under-the-curve (AUC).

Desktop fused filament fabrication (FFF) 3D printers have been growing in popularity among hobbyist and professional users as a prototyping and low-volume manufacturing tool. One issue these printers face is the inability to determine when a defect has occurred rendering the print unusable. Several techniques have been proposed to detect such defects but many of these approaches are tailored to one specific fault (e.g., filament runout/jam), use expensive hardware such as laser distance sensors, and/or use machine vision algorithms which are sensitive to ambient conditions, and hence can be unreliable. This paper proposes a versatile, reliable, and low-cost system, named MTouch, to detect millimeter-scale defects that tend to make prints unusable. At the core of MTouch is an actuated contact probe designed using a low-power solenoid, magnet, and hall effect sensor. This sensor is used to check for the presence, or absence, of the printed object at specific locations. The MTouch probe demonstrated 100% reliability, which was significantly higher than the 74% reliability achieved using a commercially available contact probe (the BLTouch). Additionally, an algorithm was developed to automatically detect common print failures such as layer shifting, bed separation, and filament runout using the MTouch probe. The algorithm was implemented on a Raspberry Pi mini-computer via an Octoprint plug-in. In head-to-head testing against a commercially available print defect detection system (The Spaghetti Detective), the MTouch was able to detect faults 44% faster on average while only increasing the print time by 8.49%. In addition, MTouch was able to detect faults The Spaghetti Detective was unable to identify such as layer shifting and filament runout/jam.

Our understanding of pull-apart basins and their fault systems has been enhanced by analog experiments and simulations. However, there has been no opportunity to compare the faults that constitute pull-apart basins with surface ruptures during earthquakes. In this study, we investigated the effects of a 2018 earthquake (Mw 7.5) on a pull-apart basin in the Palu-Koro fault system, Sulawesi Island, Indonesia, using geomorphic observations in digital elevation models, optical correlation with pre- and post-earthquake satellite images. A comparison of active fault traces determined by geomorphology with the locations of surface ruptures from the 2018 earthquake shows that some of the boundary faults of the basin are inactive and that active faulting has shifted to basin-shortcut faults and relay ramps. We also report evidence of lateral spreading, in which alluvial fan materials moved around the end of the alluvial fan. These phenomena may provide insights for anticipating the location of future surface ruptures in pull-apart basins.

For planetary gear has the characteristics of small volume, light weight and large transmission ratio, it is widely used in high speed and high power mechanical system. Poor working conditions result in frequent failures of planetary gear. A method is proposed for diagnosing faults in planetary gear based on permutation entropy of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) Adaptive Neuro-fuzzy Inference System (ANFIS) in this paper. The original signal is decomposed into 6 intrinsic mode functions (IMF) and residual components by CEEMDAN. Since the IMF contains the main characteristic information of planetary gear faults, time complexity of IMFs are reflected by permutation entropies to quantify the fault features. The permutation entropies of each IMF component are defined as the input of ANFIS, and its parameters and membership functions are adaptively adjusted according to training samples. Finally, the fuzzy inference rules are determined, and the optimal ANFIS is obtained. The overall recognition rate of the test sample used for ANFIS is 90.75%, and the recognition rate of gear with one missing tooth is relatively high. The recognition rates of different fault gears based on the method can also achieve better results. Therefore, the proposed method can be applied to planetary gear fault diagnosis effectively.

At present more than 9 million people die of cancer every year. Simple and broad-spectrum drugs are still an urgent need for cancer patients. Recently, we proposed a new hypothesis that intracellular osmotic pressure (IOP) is the driving force of cell division, and abnormal tumor proliferation is the result of uncontrolled IOP in cells. On the one hand, aneuploidy and abnormal function of Na⁺/K⁺ pump lead to a faster rise of IOP in tumor cell than normal cells, on the other hand, abnormality of cytoskeleton assembly leads to the decrease of tolerance limit of cell membrane (TLCM) of tumor cells for resisting IOP. This hypothesis predicts: 1)Tumor cells were more intolerant to hypotonic stress than normal cells. 2) Maligancies may be sellectively killed by a suddenn increase of IOP and combined with decrease of the TLCM of tumors. Na⁺/K⁺ pump inhibitors can promotely increase the IOP of tumor cells and cytoskeleton inhibitors can dramatically lower the TLCM of tumor cells. Therefore, Na⁺/K⁺ pump and cytoskeleton inhibitors may have a synergetic effect to kill tumor cells. 3) Molecules regulating cell osmolality may be new targets for cancer treatment.

In this paper a novel and flexible solution for fault prediction based on data collected from Supervisory Control and Data Acquisition (SCADA) system is presented. Generic fault/status prediction is offered by means of a data driven approach based on a self-organizing map (SOM)and the definition of an original Key Performance Indicator (KPI). The model has been assessed on a park of three photovoltaic (PV) plants with installed capacity up to 10 MW, and on more than sixty inverter modules of three different technology brands. The results indicate that the proposed method is effective in predicting incipient generic faults in average up to 7 days in advance with true positives rate up to 95%. The model is easily deployable for on-line monitoring of anomalies on new PV plants and technologies, requiring only the availability of historical SCADA data, fault taxonomy and inverter electrical datasheet.

Drought is one of the major stress factors affecting growth and development of plants. In this context, drought-related losses of crop plant productivity impede sustainable agriculture all over the world. In general, plants responses to water deficit by multiple physiological and metabolic adaptations at the molecular, cellular and organism levels. To understand the underlying mechanisms of drought tolerance, adequate stress models and arrays of reliable stress markers are required. Therefore, in this review we comprehensively address currently available models of drought stress, based on culturing plants in soil, hydroponic or agar culture. These experimental setups give access to different aspects of plant response to drought, like decrease of tissue water potential, reduction of stomata conductance and photosynthesis efficiency, accumulation of low-molecular weight solutes (metabolic adjustment) and drought protective proteins. Till now, this pattern of markers was successfully extended to the methods of enzyme chemistry, molecular biology and omics techniques. Thus, conventional tests can be efficiently complemented by determination of phytohormone and reactive oxygen species (ROS) contents, activities of antioxidant enzymes, as well as comprehensive profiling of transcriptome, proteome and metabolome.

The Doubly-Fed Induction Generator (DFIG) has significant features in comparison with Fixed Speed Wind Turbine (FSWT), which has popularized its application in power system. Due to partial rated back-to-back converters in the DFIG, Fault Ride-Through (FRT) capability improvement is one of the great subjects regarding new grid code requirements. To enhance the FRT capability of the DFIG, many studies have been carried out. Fault current limiting devices as one of the techniques are utilized to limit the current level and protect switches of the back-to-back converter from over-current damage. In this paper, a review is done based on fault current limiting characteristic of the proposed fault current limiting devices Therefore, Fault Current Limiters (FCLs) and Series Dynamic Braking Resistors (SDBRs) are mainly taken into account. Operation of all configurations including their advantages and disadvantages is explained. Impedance type and the fault current limiting devices’ location are two important factors, which significantly affect the DFIG behaviour in the fault condition. These two factors are basically studied by the simulation and their effects on the key parameters of the DFIG are investigated. Finally, future works in respect to the FCL application in the FRT improvement of the DFIG have also been discussed.

This paper introduces an approach that chooses the fault detection by calculating probabilities using probability mass function (pmf) and cumulative distribution function (CDF). This work used a method for multiple stuck-at faults by producing a new test pattern in combinational circuits. We assumed that existence of all multiple faults is only because of one single component that is faulty. A complete test set can be created by all possible single stuck-at faults in a combinational circuit using some combination of gates. The test set generation fault detection method is applied on two different 3-bit input variable and 4-bit input variable circuits. The probability of error occurrence is calculated at both 3-bit and 4-bit input variable circuits. The resulting feature is used to obtain maximum error occurrence probability to detect faults by the logic used that the complexity of the circuit is inversely proportional to the fault occurrence probability. Then again, undetectability is directly proportional to the complexity of the circuit. Therefore, finest feasible circuit should have large input variable components with less complexity to reduce the fault occurrence probability.

Simulation tools capturing the interactions of communication and electrical system operation represent a powerful support for fully assessing the potential benefits and impacts of ICT in future smart power distribution network. A strong interest is upon the possibility of exploiting the last generation communication systems for supporting the transition of distribution network towards a smart grid scenario. Having in mind the above, the authors propose a numerical co-simulation tool useful to thoroughly understand the impact of the communication networks on the performance of whole power system dynamics. The co-simulation tool has been purposely developed to simulate the highly time-critical smart grid application of fault management and network reconfiguration and permits reproducing and evaluating the behavior of the public mobile telecommunication system 4G Long Term Evolution (LTE), as communication technology for smart grid applications. Results of the paper demonstrates that LTE provides good performances for supporting the data communication required to perform fault location, extinction and a subsequent network reconfiguration in smart power distribution networks.

Abiotic stresses, including drought, salinity, cold, heat, and heavy metals, extensively reduce global agricultural production. Approaches such as conventional breeding and transgenic breeding have been widely used to cope with these environmental stresses. The clustered regularly interspaced short palindromic repeat- Cas (CRISPR/Cas) based gene-editing tool has revolutionized due to its simplicity, accessibility, adaptability, flexibility, and wide applicability. This system has a great potential to build up crop varieties with enhanced tolerance against abiotic stresses. In this review, we summarize the most recent findings on understanding the mechanism of abiotic stress response in plants and the application of CRISPR/Cas mediated gene-editing system towards enhanced tolerance to drought, salinity, cold, heat, and heavy metals stresses. Furthermore, in this review, we highlighted the recent advancements in prime editing and base editing tools for crop improvement.

Context: Code smells are associated with poor design and programming style that often degrades code quality and hampers code comprehensibility and maintainability. Goal: Identify reports from the literature that provide evidence of the influence of code smells on the occurrence of software bugs. Method: We conducted a Systematic Literature Review (SLR) to reach the~stated goal. Results: The SLR includes selected studies from July 2007 to September 2017 which analyzed the source code for open source and proprietary projects, as well, as several code smells and anti-patterns. The results of this SLR show that 24 code smells are more influential in the occurrence of bugs according to 16 studies. In contrast, three studies reported that at least 6 code smells are less influential in such occurrences. Evidence from the selected studies also point out tools, techniques and procedures applied to analyze the influence. Conclusion: To the best of our knowledge, this is the first SLR to target this goal. This study provides an up-to-date and structured understanding of the influence of code smells on the occurrence of software bugs based on findings systematically collected from a list of relevant references in the latest decade.

The present article is dealing with the state of question about building-energy monitoring systems used for data collection to estimate the Heat Lost Coefficient (HLC) with existing methods and so determinate buildings’ Envelope Energy Performance (EEP). In addition, the data requirements of HLC estimation methods are related with commonly used methods for fault detection, calibration and supervision of energy monitoring systems in buildings. Based on an extended review of experimental tests since 1978, a qualitative and quantitative analysis of Monitoring and Controlling System (MCS) specifications has been carried out. Although most actual Buildings Automation Systems (BAS) may measure the required parameters, further research is still needed to ensure that these data are accurate enough to rigorously apply the HLC estimation methods.

Having experienced large-scale disasters between 2004 and 2006, the fatalities due to large-scale disasters in 2018 were still high. In contrast, disaster risk management (DRM) and CDR in Japan have been continuously improved. Thus, there is a need to develop CDR for supporting DRM in Indonesia by learning from the Japanese experience, particularly in a disaster-prone area without large-scale disaster experience. This research was a pilot project on the development of CDR in Indonesia. The case study was a geological hazard-prone Lembang Fault area. People’s perception was collected using structured interviews, while demographic and local economic data was acquired from official statistical publications. Satellite imageries were utilized to acquire natural and built environment and land use/land cover and their changes between 2019 and 2021. Although the degrees of social capital, risk knowledge including indigenous knowledge and past disaster experience were high, government interventions on DRM and land administration are required to develop CDR in Lembang Fault area. Organized community development is expected rather than to solely involve NGOs. Moreover, strategies to develop economic resilience are needed to allow the community to bounce back from future disaster. Finally, a detail baseline data should be collected to develop DRM strategy and CDR.

Low temperature is a common biological abiotic stress in major cotton growing areas. Cold stress significantly affects the growth, yield and yield quality of cotton. Therefore, it is important to develop a more robust and cold stress tolerant cotton germplasms. Climate change and erratic climatic condition, plants have evolved various survival mechanisms, one of which induction of various stress responsive transcriptome factors, such as the C-repeat binding factor GthCBF4, which have been found to enhance cold tolerance in various plants. In this study detailed evaluation of the cotton C-repeat binding factor has been carried out. A total of29, 28, 25, 21, 30, 26 and 15 proteins encoded by the C-repeat binding factor were identified in G. herbaceum, G. arboreum, G. thurberi, G. raimondii, G. turneri, G. longicalyx and G. australe, respectively. Phylogeny evaluation revealed that the proteins were grouped into seven clades, with clade 1 and 6 being the largest. Moreover, majority of the proteins encoded by the genes were predicted to be located within. the nucleus, while some are distributed in other parts of the cell. Based on the transcriptome and RT-qPCR analysis, Gthu17439 (GthCBF4) was highly upregulated and was further validated through forward genetics. The Gthu17439 (GthCBF4) overexpressed plants showed a significantly tolerance to cold stress, with higher growth vigour compared to the wild types. The results showed that the Gthu17439 (GthCBF4) could be playing a significant role in enhancing cold stress tolerance in cotton and can be further exploited in developing a more cold stress tolerance cotton germplasm

Building a new power plant to address the growing demand for power due to population concentration in the metropolitan area is one of the world's major concerns. However, since a large power plant can not be located around the city due to burden of economic cost, building power plant outside metropolitan and cities is necessary. Therefore, new power generation facilities are promoting policies to provide distributed generator(DG) with a small capacity relatively near the metropolitan. When the DG (photovoltaic, wind farm, etc.) is connected with the grid using medium voltage direct current (MVDC) system, voltage sourced converter(VSC) should supply reactive power to the grid, because of fault ride through(FRT) operation in grid fault. If the voltage drop is severe, the converter should be disconnected from the grid immediately without supplying the reactive power, resulting in a considerable economic loss. In general, superconducting fault current limiter(SFCL) is introduced as a measure to enhance FRT capability. In this paper, we use trigger type SFCL which protects superconducting element and reduces low voltage. On the other hand, the active power unbalance of the DC-link and the DC voltage rise due to the reactive power supply of the grid-side converter. The rise of the DC voltage causes the P (active power), Q (reactive power) control of the converter to deviate, causing malfunction and damage of the DC equipment. Therefore, the rise of the DC voltage must be prevented. In this paper, we consider the suppression the DC voltage rising caused by the FRT operation through the active power tracking control (APTC).

A modular switched-capacitor (SC) DC-DC converter (MSCC) is introduced in this paper. It is designed to boost a low input voltage to a high voltage level and can be applied for photovoltaics and electric vehicles. This topology has high extensibility for high voltage gain output. The merits of the converters also lie in the fault tolerance operation and the voltage regulation with a minimum change in the duty ratio. Those features are built in when designing the modules and then integrating these into the DC-DC converter. Converter performance including voltage gain, voltage and current stress are focused and tested. The converter is modelled analytically, and its control algorithm is analyzed in detailed. Both simulation and experiment are carried out to verify the topology under normal operation and fault mode operation.

In the next decade, further digitalisation of the entire wind energy project lifecycle is expected to be a major driver for reducing project costs and risks. In this paper, a literature review on the challenges related to implementation of digitalisation in the wind energy industry is first carried out, showing that there is a strong need for new solutions that enable co-innovation within and between organisations. Therefore, a new collaboration method called the WeDoWind Ecosystem is developed and demonstrated. The method is centred around specific "challenges", which are defined by "challenge providers" within a topical "space" and made available to participants via a digital platform. The data required in order to solve a particular "challenge" is provided by the "challenge providers" under the confidentiality conditions they specify. The method is demonstrated via a case study, the EDP Wind Turbine Fault Detection Challenge. Six submitted solutions using diverse approaches are evaluated. Two of the methods perform significantly better than EDP’s existing method in terms of Total Prediction Costs (saving up to €120,000). The WeDoWind Ecosystem is found to be a promising solution for enabling co-innovation in wind energy, providing a number of tangible benefits for both challenge and solution providers.

Indonesia is an archipelago with diverse ethnicities, races, languages, cultures, beliefs, and customs. As Indonesians, with these differences, they should respect each other, or what is commonly known as tolerance. However, in the 21st century, tolerance between people in Indonesia is starting to fade. This is due to the development of the times accompanied by technology development, which causes globalization to enter quickly. The culture of tolerance typical of Indonesian society is starting to fade due to the influence of globalization. Based on these problems, this study aims to determine what causes the decline intolerance of Indonesian society in the 21st century and answer what solutions can be given to prevent the decline intolerance of Indonesian society in the 21st century. The research method used is descriptive-qualitative through literature review by analyzing data following the topic to be discussed. The data used are from 20 journal articles with a span of 2019-2021. The results of this study are in the form of a decline in intolerance that the Indonesian people own due to technological advances in the form of adverse use of social media. However, with the existence of moral education and the role of teachers and the government to overcome the decline intolerance, it is hoped that the Indonesian people will understand the meaning and importance of tolerance. This study also has limitations, namely only on the causes of the fading of tolerance in Indonesian society.

Five-day exposure of clary sage (Salvia sclarea) to 100 μM cadmium (Cd) in hydroponics was sufficient to increase Cd concentrations significantly in roots and aboveground parts and affect negatively whole plant levels of calcium (Ca) and magnesium (Mg), since Cd competes for Ca channels, while reduced Mg concentrations are associated with increased Cd tolerance. Total zinc (Zn), copper (Cu), and iron (Fe) uptake increased but their translocation to the aboveground parts decreased possible due to translocation barriers. Despite the substantial levels of Cd in leaves, without any observed defects on chloroplast ultrastructure, an enhanced photosystem II (PSII) efficiency was observed, with a higher fraction of absorbed light energy to be directed to photochemistry (ΦPSΙΙ). The concomitant increase in the photoprotective mechanism of non-photochemical quenching of photosynthesis (NPQ) resulted in an important decrease in the dissipated non-regulated energy (ΦNO), modifying the homeostasis of reactive oxygen species (ROS), through a decreased singlet oxygen (1O2) formation. Thus, when clary sage was exposed to Cd for a short period, tolerance mechanisms were triggered, with PSII photochemistry to be regulated by NPQ in such a way that PSII efficiency to be enhanced. However, exposure to a combination of Cd and high light or for longer duration (8 days) to Cd alone, resulted in an inhibition of PSII functionality pointing out towards Cd toxicity. Thus, the rapid activation of PSII functionality at short time exposures and the inhibition at longer duration suggests a hormetic response and describes these effects in terms of “adaptive response” and “toxicity”, respectively.

Candida glabrata has thoroughly adapted to successfully colonize human mucosal membranes and survive in vivo pressures prior to and during antifungal treatment. Out of all the medically relevant Candida species, C. glabrata has emerged as a leading cause of azole, echinocandin, and multidrug (MDR: azole + echinocandin) adaptive resistance. Neither mechanism of resistance is intrinsic to C. glabrata, since stable genetic resistance depends on mutation of drug target genes, FKS1 and FKS2 (echinocandin resistance), and a transcription factor, PDR1, which controls expression of major drug transporters, such as CDR1 (azole resistance). However, another hallmark of C. glabrata is the ability to withstand drug pressure both in vitro and in vivo prior to stable ‘genetic escape’. Additionally, these resistance events can arise within individual patients, which underscores the importance of understanding how this fungus is adapting to its environment and to drug exposure in vivo. Here, we explore the evolution of echinocandin resistance as a multistep model that includes general cell stress, drug adaptation (tolerance), and genetic escape. The extensive genetic diversity reported in C. glabrata will be highlighted.

As a result of the cancer immunotherapy revolution hundreds of clinical trials of the newly approved immunotherapies are now under way to improve responses. Not unexpectedly, the 2018 Nobel Prize in Physiology or Medicine was awarded to James P. Allison and Tasuku Honjo for their development of cancer therapy by blockade of co-inhibitory signals. While success stories of terminal cancer patients achieving complete remissions are accumulating, not enough research has been done into the risks of the new therapies. Since the use of immunotherapy is becoming more common, and is expected to develop into first- and second-line treatments, immunotoxicity and autoimmunity are emerging as the nemesis of immunotherapy. Immune-related adverse events (IrAEs) could affect any tissue, their incidence may reach up to 90% of patients and toxicity is dose-dependent. While the combination of two immune checkpoint inhibitors (ICIs) increased efficacy, the incidence of severe adverse events was also increased. Apparently, ICIs cannot be restricted to the targeted anti-tumor T cell population. The long lasting objective of cancer regression can only be achieved by paying a price: tolerance to healthy self tissues is compromised. In the face of an ipilimumab induced pan-lymphocytic activation, a therapeutic paradigm shift is required. The task is not desperately trying to put the genie back in the bottle by immune suppressive treatments, but instead harnessing the autoimmune forces by an off label low-dose combined anti-CTLA-4 and anti-PD1 antibody blockade, which is supplemented with conventional interleukin-2 stimulation and hyperthermia. The proof-of-principle of the low-dose-combination therapy was demonstrated in a heavily pre-treated triple negative breast cancer (TNBC) patient with far advanced pulmonary metastases and severe shortness of breath, who had exhausted all conventional treatment. Her pulmonary metastases went into complete remission with transient WHO I-II diarrhea and skin rash. She lived for 27 months after starting the low-dose-combination therapy. She had recurrence as a sternal mass and pleural metastases up to 3 cm. Since the low-dose-combination protocol consists only of approved drugs and treatments, this exceptional response should instigate further research efforts.

Compared with conventional methods of fault diagnosis for power transformers, which have defects such as imperfect encoding and too absolute encoding boundaries, this paper systematically discusses various intelligent approaches applied in fault diagnosis and decision making for large oil-immersed power transformers based on dissolved gas analysis (DGA), including expert system (EPS), artificial neural network (ANN), fuzzy theory, rough sets theory (RST), grey system theory (GST), swarm intelligence (SI) algorithms, data mining technology, machine learning (ML), and other intelligent diagnosis tools, and summarizes existing problems and solutions. From this survey, it is found that a single intelligent approach for fault diagnosis can only reflect operation status of the transformer in one particular aspect, causing various degrees of shortcomings that cannot be resolved effectively. Combined with the current research status in this field, the problems that must be addressed in DGA-based transformer fault diagnosis are identified, and the prospects for future development trends and research directions are outlined. This contribution presents a detailed and systematic survey on various intelligent approaches to faults diagnosing and decisions making of the power transformer, in which their merits and demerits are thoroughly investigated, as well as their improvement schemes and future development trends are proposed. Moreover, this paper concludes that a variety of intelligent algorithms should be combined for mutual complementation to form a hybrid fault diagnosis network, such that avoiding these algorithms falling into a local optimum. Moreover, it is necessary to improve the detection instruments so as to acquire reasonable characteristic gas data samples. The research summary, empirical generalization and analysis of predicament in this paper provide some thoughts and suggestions for the research of complex power grid in the new environment, as well as references and guidance for researchers to choose optimal approach to achieve DGA-based fault diagnosis and decision of the large oil-immersed power transformers in preventive electrical tests.

Pre-existing fracture and secondary cracks in rock mass are formed by natural power, such as magma condensed to igneous rocks and tectonic movement. The orientation and inclination of these fractures obey certain laws relating to the stress, temperature, minerals, water and so on. Therefore, cracks react differently under the same external loading on the condition of various inclination, fissure apertures, stiffness and joint roughness. To simulate the crack propagation, experiments on hollow cylinder cut by one oblique interface mimicking single fracture accumulated numerous data discovering the failure criterion in accordance with the Mohr-Coulomb criterion. And theory on the Terzaghi’s effective principle take an essential role in controlling the behavior of triggering fault. This paper introduced a series of oblique plane cutting the cylinder regarded as fractures at different inclination to concentrate on how the fracture characteristics effect the stress and strain distribution inside the specimen, especially, the relationship between displacement and water head. The key point of this numerical simulation is coupling the solid phase and the fluid phase, specifically, the mechanic and seepage field. According to the statics, curves referring to deformation and water head could be described as increasing lines. Besides, simulation on coupling solid phase and fluid phase can supply crucial evaluation on activating existing fault, and thus predicting induced seismicity in reservoirs or estimating damage in shale gas exploration.

This work aims to develop an integrated fault location and restoration approach for microgrids (MGs). This work contains two parts. Part I presents the fault location algorithm, and Part II shows the restoration algorithm. The proposed algorithms are implemented by particle swarm optimization (PSO). The fault location algorithm is based on network connection matrices, which are the modifications of bus-injection to branch-current and branch-current to bus-voltage (BCBV) matrices, to form the new system topology. The backward/forward sweep approach is used for the prefault power flow analysis. After the occurrence of fault, the voltage variation at each bus is calculated by using the Zbus modification algorithm to modify Zbus. Subsequently, the voltage error matrix is computed to search for the fault section by using PSO. After the allocation of the fault section, the multi-objective function is implemented by PSO for optimal restoration with its constraints. Finally, the IEEE 37-bus test system connected to distributed generations is utilized as the sample system for a series simulation and analysis. The outcomes demonstrated that the proposed optimal algorithm can effectively solve the fault location and restoration problem in MGs.

In this study we used a non-autonomous Chua’s Circuit, and the fractional Lorenz chaos system together with a detection method from Extension theory to analyze the voltage signals. The measured bearing signals by acceleration sensor were introduced into the master and slave systems through a Chua’s Circuit. In a chaotic system minor differences can cause significant changes that generate dynamic errors, and extension matter-element models can be used to judge the bearing conditions. Extension theory can be used to establish classical and sectional domains using the dynamic errors of the fault conditions. The results obtained were compared with those from Discrete Fourier Transform analysis, Wavelet analysis and an integer order chaos system. The diagnostic ratio showed the fractional order master and slave chaos system calculations. The results show that the method presented in this paper is very suitable for monitoring the operational state of ball bearing system to be superior to the other methods. The diagnosis ratio was better and there were other significant advantages such as low cost and few.

Reactivation of pre-existing weaknesses in the upper crust can be documented using surface features, and has occurred throughout time and space, particularly in regions where the basement material dates from the Precambrian and has undergone successive deformation events. This study aims to use surface features such as fracture patterns to document evidence of such reactivation in the Paleozoic and Cenozoic of Nebraska and Kansas (units separated by an unconformity in the study area). The most prominent basement features in southeast Nebraska and northeast Kansas are oriented NE-SW, likely related to the midcontinent rift, and oriented NW-SE, likely related to fabrics from the Central Plains Orogen. These features are well defined in the potential fields data. Fracture patterns in the study area show an E-W oriented trend, as well as clearly discernable NE-SW and subsidiary N-S and NW-SE trends. The E-W trend is interpreted to be related to far-field stresses from Laramide and Ancestral Rocky Mountain orogenic events, whilst the NE-SW trend is interpreted to be related to subtle reactivation on the Mid-continent rift and related faults, observed in basement data. These movements produced stresses of sufficient magnitude to produce extensional fractures in the overlying rock units, but not sufficient to generate shear. Similarly, the ~N-S and NW-SE fracture trends are taken as evidence of subtle reactivation on the Nemaha Uplift and Central Plains Orogen systems, generating fractures but not shear movement. This contribution therefore provides a convincing case-study of the value of fracture orientations (that is, surface morphodynamics) in discerning buried tectonic trends and subtle reactivation thereon.

In this review, we show the unique potential of spleen as an optimal site for islet transplantation and a source of mesenchymal stem cells. Islet transplantation is a cellular replacement therapy to treat severe diabetes mellitus, but its clinical outcome is unsatisfactory at present. One factor in clinical success of this therapy is selection of the most appropriate transplantation site. The spleen has been studied for a long time as a candidate site for islet transplantation. Its advantages include physiological insulin drainage and regulation of immunity. Recently it has also been shown that the spleen contributes to the regeneration of transplanted islets. The efficacy of transplantation is not as high as that obtained with intraportal transplantation, which is the current representative method of clinical islet transplantation. Safer and more effective methods of islet transplantation need to be established before the spleen can be effectively used in the clinic. Spleen also has an interesting aspect as a mesenchymal stem cell reservoir. The splenic mesenchymal stem cells contribute to tissue repair in damaged tissue, and thus, the infusion can be a promising therapy for autoimmune diseases, including type 1 diabetes mellitus and Sjogren’s syndrome.

Elevated salinity is one of the major environmental limitation factors of plant growth and development and salinity stress compromises the production and survival of plantation and urban forests and agricultural crops in the arid, semi-arid, and intertidal zones. Ulmus pumila, a salt- indigenous tree species in Asia and is widely deployed in salt-affected areas in China, and U.pumila is promising for multi-varietal forestry in plantation and urban forests. The comprehensive mechanism of the intraspecific salt tolerance is still not clear yet. Here, we investigated the physiological responses of the salinity stress based on the antioxidant enzyme activities, osmotic adjustments, and gas exchange among salt-tolerant U. pumila genotypes for 100 days under five different NaCl levels (0%, 0.3%, 0.5%, 0.7%, and 0.9% w/v) with natural surroundings and rain shade at age-2. Salt stress decreased height (HR), ground diameter (DR), and dry weight (biomass) were significantly different among genotypes. HR and performance indices were positively correlated with photosynthesis rate (Pn), apparent mesophyll conductance (AMC), and chlorophyll (CHLL) with (r= 0.7 - 0.8 ***), but were negatively related to the free proline, sugar, and protein accumulation (r=-0.5 ~ -0.7***). We found that high accumulation of sugars and more activities of SOD enzyme in leaf tissue contribute to the osmotic adjustment and ROS scavenging system under salinity treatment; the sugar content and SOD activity play key roles in U. pumila’s tolerance to salt stress, and are promising indicators for U. pumila species ex vitro selections. The ex vitro selection results align with the previous in vitro studies [37] and is promising for the MVF development.

The effect of four soil-applied sulfur [S; 100 mg S kg-1 soil (100S) and 200 mg S kg-1 soil (200S)] in different sources (elemental S, ammonium sulfate, gypsum or magnesium sulfate) in protecting mustard (Brassica juncea L. Czern & Coss.) from cadmium effects was studied. Based on the observed reduction in growth and photosynthesis in plants subjected to 100 and 200 mg Cd kg-1 soil, B. juncea cv. Giriraj was selected as the most Cd-tolerant among five cultivars (namely, Giriraj, RH-0749, Pusa Agrani, RH-406, and Pusa Tarak). Sulfur applied to soil mitigated the negative impact of Cd on sulfur assimilation, cell viability and photosynthetic functions, with a lower lipid peroxidation, electrolyte leakage, and contents of reactive oxygen species (ROS: hydrogen peroxide, H2O2, and superoxide anion, O2•−). Generally, added S caused a higher activity of antioxidant enzymes (ascorbate peroxidase, catalase and superoxide dismutase), and contents of ascorbate (AsA) and reduced glutathione (GSH), and increases in the activities of their regenerating enzymes (dehydroascorbate reductase and GSH reductase), as well as rises in S assimilation, biosynthesis of non-protein thiols (NPTs) and phytochelatins (PCs). Compared to the other S-sources tested, elemental S more prominently protected B. juncea cv. Giriraj against Cd-impacts by minimizing Cd-accumulation and its root-to-shoot translocation; decreasing cellular ROS and membrane damage, and improving Cd-chelation (NPTs and PCs), so strengthening the defense machinery against Cd. The results suggest the use of elemental S for favoring the growth and development of cultivated plants also in Cd-contaminated agricultural soils.

Leptin is the archetypal adipokine that promotes a negative whole-body energy balance largely through its action on brain leptin receptors. As such, the sustained weight loss and food intake suppression induced by Roux-en-Y gastric bypass (RYGB) surgery have been attributed to enhancement of leptin receptor signalling. We formally revisited this idea in Zucker Fatty fa/fa rats, an established genetic model of leptin receptor deficiency, and carefully compared their body weight, food intake and oral glucose tolerance after RYGB with that of sham-operated fa/fa (obese) and sham-operated fa/+ (lean) rats. We found that RYGB rats sustainably lost body weight, which converged with that of lean rats and was 25.5 % lower than that of obese rats by the end of the 4 week study period. Correspondingly, daily food intake of RYGB rats was similar to that of lean rats from the second postoperative week, while it was always at least 33.9 % lower than that of obese rats. Further, oral glucose tolerance of RYGB rats was normalized at the forth postoperative week. These findings assert that leptin is not an essential mediator of the sustained weight loss and food intake suppression as well as the improved glycemic control induced by RYGB, and instead point to additional circulating and/or neural factors.

Drought events are predicted to become more prevalent in the future. Evaluating the performance of herbicide-resistant and susceptible weed ecotypes to progressive drought can provide insights into whether resistance trait(s) increased or reduced the fitness of a resistant population. Two experiments were conducted in the greenhouse between January and May 2021 to evaluate drought tolerance differences between Palmer amaranth accessions resistant to S-metolachlor or glyphosate and their susceptible counterparts. The accessions used were: S-metolachlor-resistant (17TUN-A), a susceptible standard (09CRW-A), and glyphosate-resistant (22 to 165 EPSPS copies) and glyphosate-susceptible (3 to 10 EPSPS copies) plants from accession 16CRW-D. Daily transpiration of each plant was measured. The daily transpiration rate was converted to normalized transpiration ratio (NTR) using a double-normalization procedure. The daily soil water content was expressed as a fraction of transpirable soil water (FTSW). The threshold FTSW (FTSWcr), after which NTR decreases linearly, was estimated using a two-segment linear regression analysis. The data showed differences between S-metolachlor- resistant and -susceptible accessions (P ≤ 0.05). The FTSW remaining in the soil at the breakpoint for the S-metolachlor-susceptible accession (09CRW-A) was 0.17±0.007. The FTSW remaining in the soil at the breakpoint for the S-metolachlor-resistant accession (17TUN-A) was 0.23±0.004. Although the mechanism endowing resistance to S-metolachlor might have contributed to increased drought tolerance, follow-up experiments are needed to verify this finding. Increased EPSPS copy number did not improve drought tolerance of Palmer amaranth.

The vibration feature of weak gear fault is often covered in strong background noise, which makes it necessary to establish weak feature enhancement methods. Among the enhancement methods, stochastic resonance (SR) has the unique advantage of transferring noise energy to weak signals and has a great application prospection in weak signal extraction. But the traditional SR potential model cannot form a richer potential structure and may lead to system instability when the noise is too great. To overcome these shortcomings, the article presents a periodic potential underdamping stochastic resonance (PPUSR) method after investigating the potential function and system signal-to-noise ratio (SNR). In addition, system parameters are further optimized by using ant colony algorithm. Through simulation and gear experiments, the effectiveness of the proposed method was verified. We concluded that compared with the traditional underdamped stochastic resonance (TUSR) method, the PPUSR method had a higher recognition degree and better frequency response capability.

The objective of this study were to conduct association mapping for drought tolerance at the seedling stage and yield-related traits. 60 cowpea accessions were used in the study. Single-nucleotide polymorphisms (SNPs) discovered through genotyping by sequencing (GBS) were used for genotyping. Association mapping was conducted using single-marker regression (SMR) in Q Gene, and general linear model (GLM) and mixed linear model (MLM) built in TASSEL. The population of the cowpea accessions were analysed using STRUCTURE 2.3.4 and the peak of delta K in the greenhouse showed seven population types, whereas the peak of delta K in the glasshouse indicated the presence of six population types. One SNP marker, 14083649|F|0-9 was associated with NP with a p value <0.001. Fifty SNP markers were associated with PWT at p <0.001. Four SNP markers, 14074781|F|0-16, 100047392|F|0-36, 14083801|F|0-28 and 100051488|F|0-49 were associated with AVSPD at p <0.001. SNP markers, 14074781|F|0-16, 14083801|F|0-28 and 100051488|F|0-49 were associated with PL at P <0.001. Five SNP markers, 100047392|F|0-36, 14083801|F|0-28, 100072738|F|0-34, 14076881|F|0-49 and 14076881|F|0-49 were associated with PWDTH at p <0.001. The 65 SNP markers identified can be used in cowpea molecular breeding to select for AVSPD, NP, PL, PWDTH, PWT, and RR through marker assisted selection (MAS).

The tryptophan (TRP)-kynurenine (KYN) metabolic pathway is a main player of TRP metabolism through which more than 95% of TRP is catabolized. The pathway is activated by acute and chronic immune responses leading to a wide range of illnesses including cancer, immune diseases, neurodegenerative diseases, and psychiatric disorders. The TRP-KYN pathway synthesizes multifarious metabolites including oxidants, antioxidants, neurotoxins, neuroprotectants, and immunomodulators. The immunomodulators are known to facilitate the immune system towards a tolerogenic state, resulting in chronic low-grade inflammation (LGI) that is commonly present in obesity, poor nutrition, exposer to chemicals or allergens, prodromal stage of various illnesses, and chronic diseases. KYN, kynurenic acid, xanthurenic acid, and cinnabarinic acid are aryl hydrocarbon receptor ligands that serve as immunomodulators. Furthermore, TRP-KYN pathway enzymes are known to be activated by the stress hormone cortisol and inflammatory cytokines, and genotypic variants were observed to contribute to inflammation and thus various diseases. The tryptophan 2,3-dioxygenase, the indoleamine 2, 3-oxygenases, and the kynurenine-3-monooxygenase are main enzymes in the pathway. This review article discusses the TRP-KYN pathway with special emphasis on its interaction with the immune system and the tolerogenic shift towards chronic LGI and overviews the major symptoms, pro- and anti-inflammatory cytokines, and toxic and protective KYNs to explore the linkage between chronic LGI, KYNs, and major psychiatric, including depressive disorder, bipolar disorder, substance use disorder, post-traumatic stress disorder, schizophrenia, and autism spectrum disorder.

Kaempferia parviflora Wall. ex Baker (KP), Krachaidam in Thai or Thai ginseng, is an herbal medicine that has many potential pharmacological effects. This study focused on the oral glucose tolerance test and pharmacokinetic study in healthy volunteers administered with KP extract (90 and 180 mg/day, placebo). The oral glucose tolerance tests were performed at baselines and 28-days of administration. The pharmacokinetics were determined after a single dose administration of the tested products using 3,5,7,3,4-pentamethoxyflavone (PMF) and 5,7,4-trimethoxylfavone (TMF) as markers. The results showed that glucose metabolism via oral glucose tolerance test was not affected by KP extract. The results of pharmacokinetics study revealed that only TMF and PMF, but not DMF levels could be detected in human blood. The given doses of KP extract at 90 and 180 mg/day showed a linear dose-relationship of blood PMF concentration whereas blood TMF was detected only at high given dose (180 mg/day). The half-lives of PMF and TMF were 2–3 h. The Cmax, AUC and Tmax values of PMF and TMF estimated for the 180 mg/day dose were 85.3711.31, 73.2329.93 mg/ml; 291.8948.23, 412.20203.69 mg.h/ml; and 3.890.37, 4.500.96 h, respectively. PMF was quickly eliminated with higher Ke and Cl than TMF at the dose of 180 mg/day of KP extract. In conclusion, the results demonstrated that KP extract had no effect on glucose tolerance test. In addition, this is the first demonstration of the pharmacokinetic parameters of methoxyflavones of KP extract in healthy volunteers in a phase I study in drug development. The data suggest the safety of the KP extract and will be of benefit for further clinical trials using KP extract as food and sport supplements as well as a drug in health product development.

The assessment of rockfall risks on human activities and infrastructure is of great importance. Rock falls pose a significant risk to a) transportation infrastructure b) inhabited areas and c) Cultural Heritage sites. The paper presents a method to assess rockfall susceptibility at national scale in Greece, using a simple rating approach and GIS techniques. An extensive inventory of rockfalls for the entire country was compiled for the period between 1935 and 2019. The rockfall events that were recorded are those, which have mainly occurred as distinct rockfall episodes in natural slopes and have impacted human activities, such as roads, inhabited areas and archaeological sites. Through a detailed analysis of the recorded data, it was possible to define the factors which determine the occurrence of rockfalls. Based on this analysis, the susceptibility zoning against rockfalls at national scale was prepared, using a simple rating approach and GIS techniques. The rockfall susceptibility zoning takes into account the following parameters: (a) the slope gradient, (b) the lithology, (c) the annual rainfall intensity, (d) the earthquake intensity and (e) the active fault presence. Emphasis was given on the study of the earthquake effect as a triggering mechanism of rockfalls. Finally, the temporal and spatial frequency of the recorded events and the impact of rockfalls on infrastructure assets and human activities in Greece were evaluated.

We identify the source of the Mw = 5.6 earthquake that hit west-central Epirus on March 21, 2020 00:49:52 UTC. We use synthetic aperture radar interferograms tied to one permanent Global Navigation Satellite System (GNSS) station (GARD). We model the source by inverting the INSAR displacement data. The inversion model suggests a shallow source on a low-angle fault (39°) dipping towards east with a centroid depth of 8.5 km. The seismic moment deduced from our model agrees with those of the published seismic moment tensors. This geometry is compatible with the Margariti thrust fault within the collision zone between Apulia and Eurasia. We also processed new GNSS data and estimate a total convergence rate between Apulia and Eurasia of 8.9 mm yr-1 , of which shortening of the crust between the Epirus coastal GNSS stations and station PAXO in the Ionian Sea is equivalent to ~ 50% of it or 4.6 mm yr−1. A 60-km wide deformation zone takes up nearly most of the convergence between Apulia-Eurasia, trending N318°E. Its central axis runs along the southwest coast of Corfu, along the northeast coast of Paxos, heading toward the northern extremity of the Lefkada island.

A common fault in turbomachinery is rotor--casing rub. Shaft vibration, measured with proximity probes, is the most powerful indicator of rotor-stator rub. However, in machines such as aeroderivative turbines, with increasing industrial relevance in power generation, constructive reasons prevent the use of those sensors, being only acceleration signals at selected casing locations available. This implies several shortcomings in the characterization of the machinery condition, associated with a lower information content about the machine dynamics. In this work we evaluate the performance of the Continuous Wavelet Transform to isolate the accelerometer signal features that characterize rotor-casing rub in an aeroderivative turbine. The evaluation is carried out on a novel rotor model of a rotor flexible casing system. Due to damped transients and other short-lived features that rub induces in the signals, the Continuous Wavelet Transform proves being more effective than both Fourier and Cepstrum Analysis. This creates the chance for enabling early fault diagnosis of rub before it may cause machine shutdown or damage.

The oil and gas industry faces increasing pressure to remove people from dangerous offshore environments. Robots present a cost-effective and safe method for inspection, repair and maintenance of topside and marine offshore infrastructure. In this work, we introduce a new immobile multi-sensing robot, the Limpet, which is designed to be low-cost and highly manufacturable, and thus can be deployed in huge collectives for monitoring offshore platforms. The Limpet can be considered an instrument, where in abstract terms, an instrument is a device that transforms a physical variable of interest (measurand) into a form that is suitable for recording (measurement). The Limpet is designed to be part of the ORCA (Offshore Robotics for Certification of Assets) Hub System, which consists of the offshore assets and all the robots (UAVs, drones, mobile legged robots etc.) interacting with them. The Limpet comprises the sensing aspect of the ORCA Hub System. We integrated the Limpet with Robot Operating System (ROS), which allows it to interact with other robots in the ORCA Hub System. In this work, we demonstrate how the Limpet can be used to achieve real-time condition monitoring for offshore structures, by combining remote sensing with signal processing techniques. We show an example of this approach for monitoring offshore wind turbines. We demonstrate the use of four different communication systems (WiFi, serial, LoRa and optical communication) for the condition monitoring process. By processing the sensor data on-board, we reduce the information density of our transmissions, which allows us to substitute short-range high-bandwidth communication systems with low-bandwidth long-range communication systems. We train our classifier offline and transfer its parameters to the Limpet for online classification, where it makes an autonomous decision based on the condition of the monitored structure.

This paper presents a method to detect and classify the high impedance fault that occur in the medium voltage distribution network using discrete wavelet transform (DWT) and adaptive neuro-fuzzy inference system (ANFIS). The network is designed using Matlab software and various faults such as high impedance, symmetrical and unsymmetrical fault have been applied to study the effectiveness of the proposed ANFIS classifier method. This is achieved by training the ANFIS classifier using the features (standard deviation values) extracted from the three phase fault current signal by DWT technique for various cases of fault with different values of fault resistance in the system. The success and discrimination rate obtained for identifying and classifying the high impedance fault from the proffered method is 100% whereas the values are 66.7% and 85% respectively for conventional fuzzy based approach. The results indicate that the proposed method is more efficient to identify and discriminate the high impedance fault accurately from other power system faults in the system.

Fault diagnosis of the gearbox is a decisive part of the modern industry to find the many gearbox defects like gear tooth crack, chipped or broken, etc. But sometimes, the nonstationary properties of vibration signal and low energy of minimal faults make this procedure very challenging. Previously, many types of techniques have been developed for gearbox condition monitoring. But most of the methods are dealing with conventional techniques of the gearbox condition monitoring, such as time-domain analysis or frequency domain analysis. Most of the conventional methods are not suitable for the nonstationary vibration signal. Thus, this paper presents a novel gearbox fault diagnosis technique using conditional temporal moments and an optimizable support vector machine (SVM). This work also presents an integrated features extraction technique based on the standard features, i.e., statistical and spectral features with the combinations of moment features. The impact of the four conditional temporal moments of each gearbox condition is also presented. This work shows that the proposed method successfully classifies and categorizes the gearbox faults at an early stage.

Maize (Zea mays subsp. mays) is the staple food crop in the world. In this study, multi-layer perceptron (MLP), support vector machine (SVM), genetic algorithm-based multi-layer perceptron (MLP-GA), and genetic algorithm-based support vector machine (SVM-GA) hybrid artificial intelligence algorithms were used for the prediction of drought tolerance and stress tolerance indices in teosinte maize lines. Correspondingly, the gamma test technique was applied to determine efficient input and output vectors. The potential of developed models was evaluated based on statistical indices and graphical representation. Results of gamma test based on the least value of gamma and standard error indices show that day of anthesis (DOA), day of silking (DOS), yield index (YI), and gross yield per plant (GYP) information vector arrangements were determined as efficient information vector combination for drought-tolerant index (DTI) as well as the stress-tolerant index (STI). The results of MLP, SVM, MLP-GA, and SVM-GA algorithms were compared based on statistical indices and visual interpretation that have satisfactory for prediction of the drought-tolerant index and stress-tolerant index in maize crop. It has also seemed that genetic algorithm-based hybrid models (MLP-GA and SVM-GA) were found a better prediction of the drought-tolerant index and stress-tolerant index in maize crop. Similarly, the SVM-GA model has the highest potential to forecast the DTI and STI in maize crops as compared to MLP, SVM, MLP-GA models.

A novel adaptive neural network-based fault-tolerant control scheme is proposed for six-degree freedom nonlinear helicopter dynamic. The proposed approach can detect and mitigate sensors' faults in real-time. An adaptive observer-based on neural network (NN) and extended Kalman filter (EKF) is designed, which incorporates the helicopter's dynamic model to detect faults in the navigation sensors. Based on the detected faults, an active fault-tolerant controller, including three loops of dynamic inversion, is designed to compensate for the occurred faults in real-time. The simulation results showed that the proposed approach is able to detect and mitigate different types of faults on the helicopter navigation sensors, and the helicopter tracks the desired trajectory without any interruption.

In the past years, various intelligent machine learning and deep learning algorithms have been developed and widely applied for gearbox fault detection and diagnosis. However, the real-time application of these intelligent algorithms has been limited, mainly due to the fact that the model developed using data from one machine or one operating condition has serious diagnosis performance degradation when applied to another machine or the same machine with a different operating condition. The reason for poor model generalization is the distribution discrepancy between the training and testing data. This paper proposes to address this issue using a deep learning based cross domain adaptation approach for gearbox fault diagnosis. Labelled data from training dataset and unlabeled data from testing dataset is used to achieve the cross-domain adaptation task. A deep convolutional neural network (CNN) is used as the main architecture. Maximum mean discrepancy is used as a measure to minimize the distribution distance between the labelled training data and unlabeled testing data. The study proposes to reduce the discrepancy between the two domains in multiple layers of the designed CNN to adapt the learned representations from the training data to be applied in the testing data. The proposed approach is evaluated using experimental data from a gearbox under significant speed variation and multiple health conditions. An appropriate benchmarking with both traditional machine learning methods and other domain adaptation methods demonstrates the superiority of the proposed method.

An attractive option for realizing applications in radiation environments is to employ All-Programmable System-on-Chips (APSoCs) thanks to their high-performance computing and power efficiency merits. Despite APSoC's advantages, like any other electronic device, they are prone to radiation effects. Processors found in APSoCs must, therefore, be adequately hardened against ionizing-radiation to become a viable alternative for harsh environments. This paper proposes a novel triple-core lockstep (TCLS) approach to secure the Xilinx Zynq-7000 APSoC dual-core ARM Cortex-A9 processor against radiation-induced soft errors by coupling it with a MicroBlaze TMR subsystem in Zynq's programmable logic (PL) layer. The proposed strategy uses software-level checkpointing principles along with roll-back and roll-forward mechanisms (i.e. software redundancy), and hardware-level processor replication as well as checker circuits (i.e. hardware redundancy). Results of fault injection experiments show that the proposed solution achieved high soft error security by mitigating about 99% of bit-flips injected into both ARM cores' register data.

Feature selection is a crucial step to overcome the curse of dimensionality problem in data mining. This work proposes Recursive k-means Silhouette Elimination (RkSE) as a new unsupervised feature selection algorithm to reduce dimensionality in univariate and multivariate time-series datasets. Where k-means clustering is applied recursively to select the cluster representative features, following a unique application of silhouette measure for each cluster and a user-defined threshold as the feature selection or elimination criteria. The proposed method is evaluated on a hydraulic test rig, multi sensor readings in two different fashions: (1) Reduce the dimensionality in a multivariate classification problem using various classifiers of different functionalities. (2) Classification of univariate data in a sliding window scenario, where RkSE is used as a window compression method, to reduce the window dimensionality by selecting the best time points in a sliding window. Moreover, the results are validated using 10-fold cross validation technique. As well as, compared to the results when the classification is pulled directly with no feature selection applied. Additionally, a new taxonomy for k-means based feature selection methods is proposed. The experimental results and observations in the two comprehensive experiments demonstrated in this work reveal the capabilities and accuracy of the proposed method.

Voltage sags can cause the interruption of power supply and can negatively affect operations of customers. In this paper, the authors study the impact of battery energy storage systems (BESS) on voltage sags. A stochastic method of fault positions is used. Faults of various types are simulated and voltages are recorded. Firstly, with the BESS integrated into the network, there are higher residual voltages, fewer voltage sags and less expected critical voltage loss. Secondly, if the BESS converter power factor is reduced, recorded residual voltages are higher, voltage sags are fewer, and the number of expected critical voltage sags is lower. Finally, when three BESS converter control modes, namely constant voltage, constant power factor, and constant reactive power, were assessed, results showed similar voltage sag performances for constant power factor and constant reactive power modes. Furthermore, operating in constant voltage control outperformed the other two modes as it resulted in higher residual voltages, a lower number of voltage sags, and fewer expected critical voltage sags. The paper has demonstrated that the BESS can improve voltage sag performance. In addition, the power factor of the BESS converter and the mode of operation of the converter can influence the magnitude of the voltage sag performance improvement.

Biostimulants are materials that when applied in small amounts are capable of promoting plant growth. Nanoparticles (NPs) and nanomaterials (NMs) can be considered as biostimulants since, in specific ranges of concentration, generally in small levels, they increase the plant growth. Pristine NPs and NMS have a high density of surface charges capable of unspecific interactions with the surface charges of the cell walls and membranes of plant cells. In the same way, the functionalized NPs and NMS, and the NPs and NMs with a corona formed after the exposition to natural fluids such as water, soil solution, or the interior of organisms, presents a high density of surface charges that interact with specific charged groups in cell surfaces. The magnitude of the interaction will depend on the materials adhered to the corona, but the high-density charges located in a small volume causes an intense interaction capable of disturbing the density of surface charges of cell walls and membranes. The electrostatic disturbance can have an impact on the electrical potentials of the outer and inner surfaces, as well as on the transmembrane electrical potential, modifying the activity of the integral proteins of the membranes. The extension of the cellular response can range from biostimulation to cell death and will depend on the concentration, size, and the characteristics of the corona.

Inositol transporter (INT) is reputed as the pivotal transporter for vital metabolites like lipids, minerals, and sugars particularly. These transporters play important role in transitional metabolism and various signaling pathways in plants through regulating the transduction of messages from hormones, neurotransmitters, and immunologic and growth factors. Extensive studies have been conducted on animal INT with promising outcomes. However, few recent studies have highlighted the importance and the complexity of INT genes in the regulation of plant physiology stages including growth and tolerance to stress conditions. The present review sum-up the most recent findings on the role of INT or inositol genes in plant metabolisms and the responsive mechanisms that cope with external stressors. Moreover, we highlighted the emerging role of vacuoles and vacuolar inositol transporters in plant molecular transition and their related roles in plant growth and development. Inositol transporters are the essential mediator for the inositol uptake and its intracellular broadcasting for various metabolic pathways where they play crucial roles. Also, so far characterized only in animals, we reported evidence on Na+/inositol transporters H+/inositol symporters and suggested their roles and operating mode in plants. Thus, understanding the INT functioning system, the coordinated movement of inositol, and the relation between inositol generation and other important plant signaling pathways would be an excellent asset for advancement in researches on plant stress adaptation.

Pv11, an insect cell line established from the midge Polypedilum vanderplanki, is capable of ametabolic desiccation tolerance, so-called anhydrobiosis. We previously discovered that heat shock factor 1 (HSF1) contributes to the acquisition of desiccation tolerance by Pv11 cells, but the mechanistic details have yet to be elucidated. Here, by analyzing the gene expression profiles of newly established HSF1-knockout and -rescue cell lines, we show that HSF1 has a genome-wide effect on gene regulation in Pv11. HSF1-knockout cells exhibit a reduced desiccation survival rate, but this is completely restored in HSF1-rescue cells. By comparing mRNA profiles of the two cell lines, we reveal that HSF1 induces anhydrobiosis-related genes, especially genes encoding late embryogenesis abundant proteins and thioredoxins, but represses a group of genes involved in basal cellular processes, thus promoting an ametabolic state in the cell. In addition, HSF1 binding motifs are enriched in the promoters of anhydrobiosis-related genes and we demonstrate binding of HSF1 to these promoters by ChIP-qPCR. Thus, HSF1 directly regulates the transcription of anhydrobiosis-related genes and consequently plays a pivotal role in the induction of anhydrobiotic ability in Pv11 cells.

Upland cotton is sensitive to 2,4-dichlorophenoxyacetic acid (2,4-D), and the identification of potentially 2,4-D tolerant cotton chromosome substitution (CS) lines and understanding tolerance mechanisms provide a significant step into the development and genetic improvement of upland cotton to reduce yield loss caused by 2,4-D herbicide effects including the drifts. Experiments were conducted to understand the possible mechanism of herbicide tolerance in CS-T04-15, CS-T07, and CS-B15sh, 2,4-D herbicide-tolerant cotton CS lines compared with TM-1, the 2,4-D herbicide susceptible recurrent parent of the CS line as control, using [14C]2,4-D. Percent absorption rate and translocation patterns of the 14C-labeled herbicide application at 5.17 kBq at 6 to 48 hours after treatment (HAT) were determined. The tolerant cotton CS lines showed 15-19% [14C]2,4-D uptake while TM-1 exhibited a reduced uptake of only 1.4% [14C]2,4-D at 24 HAT. Distribution of the absorbed [14C]2,4-D showed that 2-5% was translocated outside the treated leaf. In TM-1, 77% of the herbicide was translocated above and below the treated leaf, contrasting with the reduced translocation of 14C-labeled herbicide observed in the tolerant CS lines. Interestingly, CS-T04-15 showed a restricted movement of 14C below the treated leaf at 6 to 48 HAT, suggesting a novel mechanism of herbicide tolerance. This finding is the first report on upland cotton demonstrating a complex differential uptake and translocation associated with herbicide tolerance for [14C]2,4-D in cotton CS lines.

Understanding the mechanism for heat tolerance is important for the hot pepper breeding program to develop heat-tolerant cultivars in changing climate. This study was conducted to investigate physiological and biochemical parameters related to heat tolerance and to determine leaf heat damage levels critical for selecting heat-tolerant genotypes. Seedlings of two commercial cultivars, heat-tolerant ‘NW Bigarim’ (NB) and susceptible ‘Chyung Yang’ (CY), were grown in 42 °C for ten days. Photosynthesis, electrolyte conductivity, proline content were measured among seedlings during heat treatment. Photosynthetic rate was significantly reduced in ‘CY’ but not in ‘NB’ seedlings in 42 °C. Stomatal conductivity and transpiration rate was significantly higher in ‘NB’ than ‘CY’. Proline content was also significantly higher in ‘NB’. After heat treatment, leaf heat damages were determined as 0, 25, 50 and 75% and plants with different leaf heat damages were moved to a glasshouse (30–32/22–24 °C in day/night). The growth and developmental parameters were investigated until 70 days. ‘NB’ was significantly affected by leaf heat damages only in fruit yield while ‘CY’ was in fruit set, number and yield. ‘NB’ showed fast recovery after heat stress compared to ‘CY’. These results suggest that constant photosynthetic rate via increased transpiration rate as well as high proline content in heat stress condition confer faster recovery from heat damage of heat-tolerant cultivars in seedlings stages.

Generalized stacking fault energies of palladium alloys were calculated using the density functional theory. The stacking fault energy of palladium alloys is correlated with the valence electron of the transition metal element. The twinning tendency is also modified by the presence of an alloying element in the plane of deformation. The obtained results suggest that Pd –transition metal alloys with elements such as Cr, Mo, W, Mn, Re are expected to exhibit high work hardening rate due to the tendency to emit of the partial dislocations and mechanical twins, which results in increased strength and ductility.

Background: Pre-treatment with lipopolysaccharide (LPS) protected the kidney against a later lethal ischemia. To reveal the mechanisms of renal cross-tolerance and septic acute kidney injury we investigated the effects of LPS on miRNA expression in the kidney. Methods: Male NMRI mice were injected with 40 and 10 mg/kg LPS ip. and sacrificed at 1.5 and 6 hours (early preconditioning, EP) and at 24 and 48 hours (late preconditioning, LP). The miRNA profile was established using miRCURY LNA™ microarray and confirmed with qPCR. Results: Plasma urea concentration peaked at 24 hours after LPS and decreased thereafter. Renal TNF-α and IL-6 mRNA were extremely elevated at all time-points. miRNome changes were mild at 1.5 hours, most miRNAs were altered at 6 and 24 hours and declined by 48 hours. Not all miRNAs could be assayed or validated by qPCR. In EP miR-762 was newly identified and validated and was the most elevated miRNA with both methods. In LP miR-21a-5p was the most influenced miRNA followed by miR-451a, miR-144-3p and miR-146a-5p. MiR-21a-3p increased significantly in both EP and LP. Conclusion: miR-762 might attenuate the LPS-induced immune response during EP and the miR-144/451 cluster is involved in LPS-induced renal preconditioning.

Leishmania species are parasitic protozoans which cause leishmaniasis affecting millions of people worldwide. Sterols are important components of plasma and organellar membranes and serve as precursors for the synthesis of signaling molecules. In contrast to animals, Leishmania do not produce cholesterol but instead synthesize ergostane-based sterols. C14-demethylase is a key enzyme involved in the biosynthesis of sterols in Leishmania parasites and an important drug target. Its inactivation leads to multiple defects including increased plasma membrane fluidity, mitochondrion dysfunction, hypersensitivity to stress and reduced virulence. In this study, we revealed a novel role for sterol synthesis in the maintenance of RNA stability and translation. Sterol alteration in C14-demethylase knockout mutant leads to increased RNA degradation, reduced translation and impaired heat shock response. Thus, sterol biosynthesis plays an unexpected role in global gene regulation in Leishmania parasites.

Viral infections are a natural part of our existence. They can affect us in many ways that are the result of the interaction between the viral pathogen and our immune system. Most times the resulting immune response is beneficial for the host. The pathogen gets cleared thus protecting our vital organs with no other consequences. Conversely, the reaction of our immune system against the pathogen can cause organ damage (immunopathology) or lead to autoimmune disease. To date, there are several mechanisms for virus-induced autoimmune disease, including molecular mimicry and bystander activation, in support of the “fertile field” hypothesis, terms defined in our review. On the flip side, viral infections have been associated with protection from autoimmunity through mechanisms that include Treg invigoration and immune deviation, in support of the “hygiene hypothesis”, also defined here. Infection with lymphocytic choriomeningitis virus (LCMV) is one of the prototypes showing that the interaction of our immune system with viruses can either accelerate or prevent autoimmunity. Studies using mouse models of LCMV have helped conceive and establish several concepts that we today know and explain how viruses can lead to autoimmune activation or induce tolerance. Some of the most important mechanisms established during the course LCMV are described in this short review.

The fault diagnosis of safety critical systems such as wind turbine installations includes extremely challenging aspects that motivate the research issues considered in this paper. In fact, the prompt detection and the reliable diagnosis of faults in their earlier occurrence represent the key point especially for offshore installations. For these plants, operation and maintenance procedures in harsh environments would inevitably increase the cost of the energy production. Therefore, this work investigates fault diagnosis solutions that are considered in a viable way and used as advanced techniques for condition monitoring of dynamic processes. To this end, the work proposes the design of fault diagnosis strategies that exploit the estimation of the fault by means of data--driven approaches. This solution leads to the development of effective methods allowing the management of partially unknown information of the system dynamics, while coping with measurement errors, the model--reality mismatch and other disturbance effects. In mode detail, the proposed data--driven methodologies exploit fuzzy systems and neural networks in order to estimate the nonlinear dynamic relations between the input and output measurements of the considered process and the faults. To this end, the fuzzy and neural network structures are integrated with auto--regressive with exogenous input descriptions, thus making them able to approximate unknown nonlinear dynamic functions with arbitrary degree of accuracy. Once these models are estimated from the input and output data measurement acquired from the considered dynamic process, the capabilities of their fault diagnosis capabilities are validated by using a high--fidelity benchmark that simulates the healthy and the faulty behaviour of a wind turbine system. Moreover, at this stage the benchmark is also useful to analyse the robustness and the reliability characteristics of the developed tools in the presence of model--reality mismatch and modelling error effects featured by the wind turbine simulator. On the other hand, a hardware--in--the--loop tool is finally implemented for testing and comparing the performance of the developed fault diagnosis strategies in a more realistic environment and with respect to different fault diagnosis approaches.

Based on the topology of wide area protection system (WAPS), after studying the reliability of hardware system and information flow in the WAPS and establishing the reliability assessment model, the multi-service reliability analysis method with multi monitoring and protection tasks in WAPS was proposed. In the model, the impact of network quality of service (QoS) such as information flow loss and delay, is studied. On the base of the model, the multi-service reliability evaluation method is employed to analyze the reliability of a WAPS of IEEE14 node power system, and the key nodes of the WAPS is given, which provides a basis for improving the reliability of the WAPS.

Although the failure of antibiotic treatment is normally attributed to resistance, tolerance and persistence display a significant role in the lack of response to antibiotics. Due to the fact that several nosocomial pathogens show a high level of tolerance and/or resistance to chlorhexidine, in this study we analyzed the molecular mechanisms associated with chlorhexidine adaptation in two clinical strains of Klebsiella pneumoniae by phenotypic and transcriptomic studies. These two strains belong to ST258-KPC3 (high-risk clone carrying β-lactamase KPC3) and ST846-OXA48 (low-risk clone carrying β-lactamase OXA48). Our results showed that K. pneumoniae ST258-KPC3CA and ST846-OXA48CA strains exhibited a different behavior under CHLX pressure, adapting to this biocide through resistance and tolerance mechanisms, respectively. Furthermore, the appearance of cross-resistance to colistin was observed in the ST846-OXA48CA strain (tolerant to CHLX), using the broth microdilution method. Interestingly, this ST846-OXA48CA isolate contained a plasmid that encodes a novel type II toxin/antitoxin (TA) system, PemK/PemI. We characterized this PemK/PemI TA system by cloning both genes into the IPTG-inducible pCA24N plasmid, and found their role in persistence and biofilm formation. Accordingly, the ST846-OXA48CA strain showed a persistence biphasic curve in the presence of a chlorhexidine-imipenem combination, and these results were confirmed by the enzymatic assay (WST-1).

The fault diagnosis of wind turbine systems represent a challenging issue, especially for offshore installations, thus justifying the research topics developed in this work. Therefore, this paper addresses the problem of the fault diagnosis of wind turbines, and it present viable solutions of fault detection and isolation techniques. The design of the so--called fault indicator consists of its estimate, which involves data--driven methods, as they result effective tools for managing partial analytical knowledge of the system dynamics, together with noise and disturbance effects. In particular, the suggested data--driven strategies exploit fuzzy systems and neural networks that are employed to determine nonlinear links between measurements and faults. The selected architectures are based on nonlinear autoregressive with exogenous input prototypes, as they approximate the dynamic evolution of the system along time. The designed fault diagnosis schemes are verified via a high--fidelity simulator, which describes the normal and the faulty behaviour of an offshore wind turbine plant. Finally, by taking into account the presence of uncertainty and disturbance implemented in the wind turbine simulator, the robustness and the reliability features of the proposed methods are also assessed. This aspect is fundamental when the proposed fault diagnosis methods have to be applied to offshore installations.

The induced draft (ID) fan is important auxiliary equipment in the thermal power plant. It is of great significance to monitor the operation of the ID fan for safe and efficient production. In this paper, an adaptive warning model is proposed to detect early faults of ID fans. First, a non-parametric monitoring model is constructed to describe the normal operation states with the multivariate state estimation technique (MSET). Then, an early warning approach is presented to identify abnormal behaviors based on the results of the MSET model. As the performance of the MSET model is heavily influenced by the normal operation data in the historic memory matrix, an adaptive strategy is proposed by using the samples with a high data quality index (DQI) to manage the memory matrix and update the model. The proposed method is applied to a 300 MW coal-fired power plant for early fault detection, and it is compared with the model without an update. Results show that the proposed method can detect the fault earlier and more accurately.

Water stress (WS) and heat stress (HS) have a negative effect on soybean plant growth and crop productivity. During WS, soybean plants opt for survival through ion homeostasis and the conformations of proteins are disconcerted as plant cells lose water while HS leads to difficulties in flowering and fruiting. Some of these changes include oxidative stress leading to the destruction of photosynthetic apparatus, macromolecules within cells and the onset of complex signaling cascades. Changes in the physiological characteristics, proteome, and certain metabolites investigated on molecular and cellular functions were studied in two soybean cultivars exposed to different heat and water stress conditions independently and in combination. Leaf protein composition was studied using 2-DE and complemented with MALDI TOF mass spectrometry. While two cultivars displayed genetic variation in response to water and heat stress, thirty-nine proteins were significantly altered in their relative abundance in response to WS, HS and combined WS+HS in both cultivars; a majority of them involved in metabolism, response to heat and photosynthesis showing significant cross-tolerance mechanisms. Functional analysis revealing a majority of heat responsive-proteins were more abundant during HS and combined stress (WS+HS) whereas these proteins were low to WS in cultivar PI 471938 and heat shock proteins were in low abundance to water, heat and combined stresses in cultivar R95-1705. Most protein abundances were not correlated with their expression at mRNA levels in PI cultivar, however, in cultivar R 95, the expression levels of transcript follow their relative abundance in proteins. Our systems bioinformatics analyses revealed that MED37C, a probable mediator of RNA polymerase transcription II protein showed potential interacting partners in Arabidopsis and our studies signifies the marked impact of this protein in PI cultivar. Elevated activities in antioxidant enzymes indicate that the PI-371938 cultivar has the ability to restore the oxidation levels and sustain the plant during the stress. Our study hypothesizes the plant’s development of cross-stress tolerance which will help foster the ongoing ventures in genetic modifications in stress tolerance.

Uridine diphosphate (UDP)-glycosyltransferases (UGTs) are major phase II detoxification enzymes involved in glycosylation of lipophilic endobiotics and xenobiotics, including phytoalexins. Nicotine, one of the most abundant secondary plant metabolites, is highly toxic to herbivorous insects. Plant-herbivore competition has been the major impetus for evolution of large superfamilies of UGTs and other detoxification enzymes. However, UGT functions in Myzus persicae adaptation have remained unknown. In this study, UGT inhibitors (sulfinpyrazone and 5-nitrouracil) significantly increased nicotine toxicity in M. persicae nicotianae, suggesting UGTs may be involved in nicotine tolerance. One hundred one UGT transcripts identified from the M. persicae genome/transcriptome were renamed according to UGT Nomenclature Committee guidelines and clustered into 11 families: UGT329, UGT330, UGT339, UGT341-UGT345, and UGT348-UGT350. UGT344 contained the most UGTs (fifty-seven). Ten UGTs (UGT330A3, UGT339A2, UGT341A6, UGT342B3, UGT343C3, UGT344D5, UGT344D8, UGT348A3, UGT349A3 and UGT350A3) were overexpressed in M. persicae nicotianae compared with M. persicae sensu stricto. UGT330A3/UGT344D5/UGT348A3/UGT349A3 knockdown significantly increased M. persicae nicotianae nicotine sensitivity, suggesting UGT upregulation in this subspecies may be associated with nicotine tolerance and thus host adaptation. This study reveals possible UGTs relevant to nicotine adaptation in tobacco-consuming M. persicae nicotianae and might facilitate further validation of the roles of these UGTs in nicotine tolerance.

Low temperatures in the autumn induce enhanced expression/relative accumulation of several cold-inducible transcripts/proteins with protective functions from Late-embryogenesis-abundant (LEA) superfamily including dehydrins. Several studies dealing with plants grown under controlled conditions revealed a correlation (significant quantitative relationship) between dehydrin transcript/protein relative accumulation and plant frost tolerance. However, to apply these results in breeding, field experiments are necessary. The aim of the review is to provide a summary of the studies dealing with the relationships between plant acquired frost tolerance and COR/LEA transcripts/proteins relative accumulation in cereals grown in controlled and in the field conditions. The impacts of cold acclimation and vernalization processes on the ability of winter-type Triticeae to accumulate COR/LEA proteins are discussed. The factors determining dehydrin relative accumulation under controlled cold acclimation treatments versus field trials during winter seasons are discussed. In conclusion, it can be stated that dehydrins could be used as suitable indicators of winter surival in field-grown winter cereals but only in plant prior to the fullfilment of vernalization requirement.

Keywords: Chemical Intolerance, Drug Intolerance, Food Intolerance, QEESI, BREESI, Multiple Chemical Sensitivity, Toxicant-induced Loss of Tolerance, Prevalence